Análisis de Red Bipartita: Noticias - Usuarios
library(igraph)##
## Attaching package: 'igraph'## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
##
## unionCargamos el dataset
En primera medida, decidimos samplear el dataset de BuzzFeedNewsUser.txt, ya que la cantidad de nodos y links era demasiado grande para poder trabajar con el en nuestras computadoras. Este dataset cuenta con tres columnas: 1. El id de la noticia 2. El id del usuario 3. Cantidad de posteos/retweets del usuario sobre esa noticia.
Decidimos eliminar la columna del punto tres para simplificar el análisis. Si quisieramos profundizar el análisis, podríamos usar esta tercera columna como los pesos de la red bipartita.
BF_news_users <- read.csv("../DatosKaggle/BuzzFeed/sample/BuzzFeedNewsUser_newshash_20210815.csv", header=FALSE, encoding = "utf-8-sig")
BF_news_users <- BF_news_users[-1,]
BF_news_users$V1 = as.character(BF_news_users$V1)
g <- graph.data.frame(BF_news_users)Utilizando bipartite.mapping podemos diferenciar a cada tipo de nodo. Esto requiere que inicialmente los tipos de datos que le pasemos a bipartite.mapping sean distintos. En este caso esto ya esta solucionado. Los ids de usuarios son numéricos, mientras que los ids de las noticias son un hash único creado en el procesamiento del dataset en el Jupyter Notebook.
bipartite.mapping(g)## $res
## [1] TRUE
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## [1081] TRUE TRUEV(g)$type <- bipartite_mapping(g)$typeV(g)$color <- ifelse(V(g)$type, "lightblue", "salmon")
V(g)$shape <- ifelse(V(g)$type, "circle", "square")
E(g)$color <- "lightgray"
plot(g,
vertex.label = NA,
vertex.size = 3,
edge.arrow.size=0.1,
vertex.label.color = "black",
layout=layout_components)
Los nodos cuadrados representan los usuarios y los nodos circulares celestes representan las noticias.
Creación de Redes
Utilizando bipartite_projection() creamos la red de usuarios-usuarios y de noticias-noticias
user-user: Red que representa el número de usuarios que cada noticia tiene en común
news-news: Red que representa el número de noticias que cada usuario tiene en común
projection_g <- bipartite_projection(g, multiplicity = TRUE)
users_projected <- projection_g$proj1
news_projected <- projection_g$proj2V(news_projected)$color <- "lightblue"
V(news_projected)$shape <- "circle"
E(news_projected)$color <- "lightgray"
plot(news_projected,
vertex.label = NA,
vertex.size = 3,
edge.arrow.size=0.1,
vertex.label.color = "black",
layout=layout_components)
V(users_projected)$color <- "salmon"
V(users_projected)$shape <- "square"
E(users_projected)$color <- "lightgray"
plot(users_projected,
vertex.label = NA,
vertex.size = 3,
edge.arrow.size=0.1,
vertex.label.color = "black",
layout=layout_components)
Análisis de las redes
Proyección: Red Noticias-Noticias
Análisis de los componentes de la red
connected_components = components(news_projected)
connected_components## $membership
## A8E571 F466AA 9C8FAF B4EA27 229D27 F6430A 8B4AE4 7EB31E 6513A0 CA534D 19776E
## 1 1 1 1 1 1 1 1 1 1 1
## C2F13C 5C5CB8 232D46 3D02CC 5D1A52 8FFAFB 7E4715 A26558 92F413 3A6ED0 78D9FA
## 1 1 1 1 1 1 1 1 1 1 1
## 581460 633F13 535FB1 719576 C3D240 7253B0 A0E7B2 CCFD5A 3DBF7A 290C3D 57EF1C
## 1 1 1 1 1 1 1 1 1 1 1
## 577445 2C2ED2 AB6B13 24CE0B
## 1 1 1 1
##
## $csize
## [1] 37
##
## $no
## [1] 1connected_components$no## [1] 1connected_components$csize## [1] 37Medidas de Centralidad Basadas en Primeros Vecinos
Degree Centrality
degree_centrality = degree(news_projected)
sort(degree_centrality)## CA534D CCFD5A 2C2ED2 AB6B13 6513A0 3DBF7A 290C3D 57EF1C 24CE0B 7EB31E F466AA
## 1 2 2 2 3 3 3 3 3 4 5
## 7E4715 C3D240 577445 3D02CC 3A6ED0 7253B0 5D1A52 719576 19776E 78D9FA 633F13
## 5 5 5 6 6 6 7 7 8 8 9
## 8B4AE4 A0E7B2 9C8FAF 229D27 C2F13C 5C5CB8 F6430A 232D46 A26558 92F413 A8E571
## 11 11 12 12 12 12 13 13 13 13 14
## 8FFAFB 581460 535FB1 B4EA27
## 14 14 15 18max(degree_centrality)## [1] 18El nodo con mayor degree es B4EA27, el Cuál representa a la noticia, catalogada como real, con título: National poll: Clinton leads Trump by 6. Le siguen:
- 535FB1: Barack Obama at Benjamin Netanyahu meeting: ‘He is always very candid with us’ (Real)
- 581460: Sacramento mayor punches pie thrower (Real)
- 8FFAFB: “Donald Trump: Drugs a ‘Very, Very Big Factor’ in Charlotte Protests” (Real)
- A8E571: Federal Agents Make Massive Discovery at Southern Border… ISIS Is Here ⋆ Freedom Daily (Fake)
hist(unname(degree_centrality),labels = TRUE, xlim = c(0,18), ylim = c(0,8), xlab = "Degree", main = "Histograma de los Degree")
A continuación graficamos la proyección de la red bipartita en función de las noticias. Cada nodo tiene asignado un color que coincide con el degree del mismo:
colorfunc <- colorRampPalette(c( "Medium Slate Blue",
"Magenta",
"Maroon 2",
"Purple",
"red",
"firebrick",
"lightcoral",
"saddlebrown",
"tomato",
"darkorange",
"gold",
"lightgreen",
"mediumspringgreen",
"dodgerblue",
"steelblue",
"Light Steel Blue 1",
"Royal Blue 2"))
colors <- colorfunc(max(degree_centrality))
V(news_projected)$color <- colors[degree_centrality]
V(news_projected)$label <- degree_centrality
V(news_projected)$shape <- "circle"
E(news_projected)$color <- "lightgray"
plot(news_projected,
vertex.size = 8,
edge.arrow.size=0.1,
layout=layout_components,
main = "Proyección Red Noticias: Nodos según el Degree")
El in-degree y out-degree es el mismo dado que la red no es dirigida.
Eigenvector Centrality
Esta métrica nos da una intuición de la influencia que un nodo tiene en la red. Si un nodo es alcanzado por muchos otros nodos (que además tienen una alta eigenvector centrality) ese nodo va a tener un eigenvector centrality mayor.
Podríamos decir que eigenvector centrality calcula el acceso indirecto al poder de un determinado nodo.
eigenvector_cetrality <- evcent(news_projected)$vector
eigenvector_cetrality## A8E571 F466AA 9C8FAF B4EA27 229D27 F6430A
## 4.747534e-03 3.104493e-02 7.315087e-01 1.000000e+00 3.018739e-01 6.599773e-01
## 8B4AE4 7EB31E 6513A0 CA534D 19776E C2F13C
## 1.458165e-03 1.524449e-02 5.646678e-03 4.155654e-03 2.000607e-04 2.203432e-01
## 5C5CB8 232D46 3D02CC 5D1A52 8FFAFB 7E4715
## 5.724806e-01 3.123656e-01 8.137546e-04 1.082638e-03 8.102800e-01 4.118663e-03
## A26558 92F413 3A6ED0 78D9FA 581460 633F13
## 2.825551e-01 7.730089e-01 3.997119e-03 3.575415e-04 3.261817e-01 1.670851e-04
## 535FB1 719576 C3D240 7253B0 A0E7B2 CCFD5A
## 2.168248e-01 1.000296e-04 1.262598e-05 3.376097e-05 2.025147e-01 1.515582e-02
## 3DBF7A 290C3D 57EF1C 577445 2C2ED2 AB6B13
## 9.548266e-05 9.548266e-05 2.670758e-02 5.611975e-02 5.851409e-02 1.286112e-04
## 24CE0B
## 1.246838e-04hist(eigenvector_cetrality, labels = TRUE, ylim = c(0,30), xlab = "Eigenvector Centrality", main = "Histograma de los Valores de Eigenvector Centrality")
Según este histograma, hay dos nodos que tienen un eigenvector centrality mayor a 0.8. Veamos cuales son:
which(eigenvector_cetrality >=0.8)## B4EA27 8FFAFB
## 4 17- B4EA27: National poll: Clinton leads Trump by 6 (Real)
- 8FFAFB: “Donald Trump: Drugs a ‘Very, Very Big Factor’ in Charlotte Protests” (Real)
colorfunc <- colorRampPalette(c( "Medium Slate Blue",
"Magenta",
"Maroon 2",
"Purple",
"red",
"firebrick",
"lightcoral",
"saddlebrown",
"tomato",
"darkorange",
"gold",
"lightgreen",
"mediumspringgreen",
"dodgerblue",
"steelblue",
"Light Steel Blue 1",
"Royal Blue 2"))
scaled_eigen = (eigenvector_cetrality * 10) +1
colors <- colorfunc(max(scaled_eigen))
V(news_projected)$color <- colors[scaled_eigen]
V(news_projected)$shape <- "circle"
E(news_projected)$color <- "lightgray"
plot(news_projected,
vertex.label = format(round(eigenvector_cetrality, 2), nsmall = 1),
vertex.size = 12,
edge.arrow.size=0.1,
layout=layout_components,
main = "Proyección Red Noticias: Nodos según el Eigenvector Centrality")
Pagerank Centrality
page_rank = page.rank(news_projected)$vector
page_rank## A8E571 F466AA 9C8FAF B4EA27 229D27 F6430A
## 0.083888781 0.008883901 0.045454404 0.089577439 0.028207245 0.046552555
## 8B4AE4 7EB31E 6513A0 CA534D 19776E C2F13C
## 0.052409647 0.012176086 0.008384935 0.004746437 0.022851581 0.020549795
## 5C5CB8 232D46 3D02CC 5D1A52 8FFAFB 7E4715
## 0.040725333 0.025164500 0.038602081 0.046803996 0.051943193 0.028722855
## A26558 92F413 3A6ED0 78D9FA 581460 633F13
## 0.030139016 0.048326124 0.025849669 0.026306999 0.033330557 0.018378688
## 535FB1 719576 C3D240 7253B0 A0E7B2 CCFD5A
## 0.026190228 0.017189248 0.012737391 0.014201158 0.017787098 0.008779622
## 3DBF7A 290C3D 57EF1C 577445 2C2ED2 AB6B13
## 0.013500548 0.013500548 0.007574367 0.006863394 0.007834791 0.006678291
## 24CE0B
## 0.009187501hist(page_rank, labels = TRUE, ylim = c(0,10), xlim = c(0,0.1), xlab = "PageRank Centrality", main = "Histograma de los Valores de PageRank Centrality")
Según este histograma, hay dos noticias que poseen una centralidad relativamente alta. Veamos cuales son:
which(page_rank >= 0.08)## A8E571 B4EA27
## 1 4- A8E571: Federal Agents Make Massive Discovery at Southern Border… ISIS Is Here ⋆ Freedom Daily (Fake)
- B4EA27: National poll: Clinton leads Trump by 6 (Real)
En el siguiente gáfico, graficamos la red y cada nodo tiene como label el page ranks score pero escalado, ya que es más fácil visualizar de esa forma.
colorfunc <- colorRampPalette(c( "Medium Slate Blue",
"Magenta",
"Maroon 2",
"Purple",
"red",
"firebrick",
"lightcoral",
"saddlebrown",
"tomato",
"darkorange",
"gold",
"lightgreen",
"mediumspringgreen",
"dodgerblue",
"steelblue",
"Light Steel Blue 1",
"Royal Blue 2"))
scaled_page_rank = (page_rank * 1000)
colors <- colorfunc(max(scaled_page_rank))
V(news_projected)$color <- colors[scaled_page_rank]
V(news_projected)$shape <- "circle"
E(news_projected)$color <- "lightgray"
plot(news_projected,
vertex.label = format(round(scaled_page_rank), nsmall = 1),
vertex.size = 12,
edge.arrow.size=0.1,
layout=layout_components,
main = "Proyección Red Noticias: Nodos según Page Rank Score Escalado")
Authorities & Hubs
Authorities
authority = authority.score(news_projected)$vector
authority## A8E571 F466AA 9C8FAF B4EA27 229D27 F6430A
## 4.747534e-03 3.104493e-02 7.315087e-01 1.000000e+00 3.018739e-01 6.599773e-01
## 8B4AE4 7EB31E 6513A0 CA534D 19776E C2F13C
## 1.458165e-03 1.524449e-02 5.646678e-03 4.155654e-03 2.000607e-04 2.203432e-01
## 5C5CB8 232D46 3D02CC 5D1A52 8FFAFB 7E4715
## 5.724806e-01 3.123656e-01 8.137546e-04 1.082638e-03 8.102800e-01 4.118663e-03
## A26558 92F413 3A6ED0 78D9FA 581460 633F13
## 2.825551e-01 7.730089e-01 3.997119e-03 3.575415e-04 3.261817e-01 1.670851e-04
## 535FB1 719576 C3D240 7253B0 A0E7B2 CCFD5A
## 2.168248e-01 1.000296e-04 1.262598e-05 3.376097e-05 2.025147e-01 1.515582e-02
## 3DBF7A 290C3D 57EF1C 577445 2C2ED2 AB6B13
## 9.548266e-05 9.548266e-05 2.670758e-02 5.611975e-02 5.851409e-02 1.286112e-04
## 24CE0B
## 1.246838e-04hist(authority, labels = TRUE, ylim = c(0,30), xlim = c(0,1), xlab = "Authority Score", main = "Histograma de los Valores de Authority Score")
según este histograma, hay dos noticias que poseen un authority relativamente alta. Veamos cuales son:
which(authority >= 0.8)## B4EA27 8FFAFB
## 4 17- B4EA27: National poll: Clinton leads Trump by 6 (Real)
- A8E571: Federal Agents Make Massive Discovery at Southern Border… ISIS Is Here ⋆ Freedom Daily (Fake)
colorfunc <- colorRampPalette(c( "red",
"firebrick",
"lightcoral",
"saddlebrown",
"tomato",
"darkorange",
"gold",
"lightgreen",
"mediumspringgreen",
"dodgerblue",
"steelblue"))
scaled_authority = authority *1000000
colors <- colorfunc(max(scaled_authority))
V(news_projected)$color <- colors[scaled_authority]
V(news_projected)$shape <- "circle"
E(news_projected)$color <- "lightgray"
plot(news_projected,
vertex.label = format(round(authority, 2), nsmall = 1),
vertex.label.color = "Black",
vertex.size = 14,
edge.arrow.size=0.1,
layout = layout_components,
main = "Proyección Red Noticias: Nodos según Authority Score")
Hubs
hubs = hub.score(news_projected)$vector
hubs## A8E571 F466AA 9C8FAF B4EA27 229D27 F6430A
## 4.747534e-03 3.104493e-02 7.315087e-01 1.000000e+00 3.018739e-01 6.599773e-01
## 8B4AE4 7EB31E 6513A0 CA534D 19776E C2F13C
## 1.458165e-03 1.524449e-02 5.646678e-03 4.155654e-03 2.000607e-04 2.203432e-01
## 5C5CB8 232D46 3D02CC 5D1A52 8FFAFB 7E4715
## 5.724806e-01 3.123656e-01 8.137546e-04 1.082638e-03 8.102800e-01 4.118663e-03
## A26558 92F413 3A6ED0 78D9FA 581460 633F13
## 2.825551e-01 7.730089e-01 3.997119e-03 3.575415e-04 3.261817e-01 1.670851e-04
## 535FB1 719576 C3D240 7253B0 A0E7B2 CCFD5A
## 2.168248e-01 1.000296e-04 1.262598e-05 3.376097e-05 2.025147e-01 1.515582e-02
## 3DBF7A 290C3D 57EF1C 577445 2C2ED2 AB6B13
## 9.548266e-05 9.548266e-05 2.670758e-02 5.611975e-02 5.851409e-02 1.286112e-04
## 24CE0B
## 1.246838e-04hist(hubs, labels = TRUE, ylim = c(0,30), xlim = c(0,1), xlab = "Hub Score", main = "Histograma de los Valores de Hub Score")
Según este histograma, hay dos noticias que poseen un authority relativamente alta. Veamos cuales son:
which(hubs >= 0.8)## B4EA27 8FFAFB
## 4 17- B4EA27: National poll: Clinton leads Trump by 6 (Real)
- A8E571: Federal Agents Make Massive Discovery at Southern Border… ISIS Is Here ⋆ Freedom Daily (Fake)
colorfunc <- colorRampPalette(c( "red",
"firebrick",
"lightcoral",
"saddlebrown",
"tomato",
"darkorange",
"gold",
"lightgreen",
"mediumspringgreen",
"dodgerblue",
"steelblue"))
scaled_hubs = hubs * 1000000
colors <- colorfunc(max(scaled_hubs))
V(news_projected)$color <- colors[scaled_hubs]
V(news_projected)$shape <- "circle"
E(news_projected)$color <- "lightgray"
plot(news_projected,
vertex.label = format(round(hubs, 2), nsmall = 1),
vertex.label.color = "Black",
vertex.size = 14,
edge.arrow.size=0.1,
layout = layout_components,
main = "Proyección Red Noticias: Nodos según Hubs Score")
Medidas de Centralidad Basadas en Caminos más Cortos
Closeness Centrality
closeness_centrality = closeness(news_projected)
closeness_centrality## A8E571 F466AA 9C8FAF B4EA27 229D27 F6430A
## 0.012195122 0.008928571 0.009803922 0.010000000 0.008771930 0.010526316
## 8B4AE4 7EB31E 6513A0 CA534D 19776E C2F13C
## 0.010416667 0.010204082 0.007692308 0.006711409 0.007874016 0.008849558
## 5C5CB8 232D46 3D02CC 5D1A52 8FFAFB 7E4715
## 0.010869565 0.010752688 0.007352941 0.007633588 0.010752688 0.010309278
## A26558 92F413 3A6ED0 78D9FA 581460 633F13
## 0.008771930 0.010309278 0.011363636 0.007751938 0.009259259 0.010204082
## 535FB1 719576 C3D240 7253B0 A0E7B2 CCFD5A
## 0.012987013 0.009708738 0.007812500 0.008547009 0.009900990 0.007462687
## 3DBF7A 290C3D 57EF1C 577445 2C2ED2 AB6B13
## 0.008849558 0.008849558 0.008474576 0.008849558 0.005434783 0.009009009
## 24CE0B
## 0.008928571hist(closeness_centrality, labels = TRUE, ylim = c(0,11), xlim = c(0.004,0.014), xlab = "Closeness Centrality", main = "Histograma de los Valores de Closeness Centrality")
Graficamos a continuación la red de noticias-noticias donde cada nodo tiene el tamaño y color asociado a la métrica de closeness calculada anteriormente.
colorfunc <- colorRampPalette(c( "red",
"firebrick",
"lightcoral",
"saddlebrown",
"tomato",
"darkorange",
"gold",
"lightgreen",
"mediumspringgreen",
"dodgerblue",
"steelblue"))
scaled_closeness = closeness_centrality * 1000
colors <- colorfunc(max(scaled_closeness))
V(news_projected)$color <- colors[scaled_closeness]
V(news_projected)$shape <- "circle"
E(news_projected)$color <- "lightgray"
plot(news_projected,
vertex.label = format(round(closeness_centrality, 4), nsmall = 1),
vertex.labe.color = "black",
vertex.size = closeness_centrality *1000 / 2,
edge.arrow.size=0.1,
layout = layout_components,
main = "Proyección Red Noticias: Nodos Según Closeness Centrality")
plot(news_projected,
vertex.label = NA,
vertex.size = closeness_centrality *1000 / 2,
edge.arrow.size=0.1,
layout = layout_components,
main = "Proyección Red Noticias: Nodos Según Closeness Centrality")
Betweenness Centrality
betweenness_centrality <- betweenness(news_projected)
betweenness_centrality## A8E571 F466AA 9C8FAF B4EA27 229D27 F6430A
## 176.4977723 24.4825002 7.4967341 81.0948007 8.7889510 13.5588993
## 8B4AE4 7EB31E 6513A0 CA534D 19776E C2F13C
## 53.0275825 68.5657207 4.4201801 0.0000000 9.4333333 7.5729006
## 5C5CB8 232D46 3D02CC 5D1A52 8FFAFB 7E4715
## 25.5415037 35.9062866 2.1666667 0.0000000 46.3262140 32.4954885
## A26558 92F413 3A6ED0 78D9FA 581460 633F13
## 38.0216783 13.3910672 61.6611451 0.0000000 47.8756363 85.4078453
## 535FB1 719576 C3D240 7253B0 A0E7B2 CCFD5A
## 257.8292203 38.6850840 0.9000000 10.4333333 21.0681627 0.0000000
## 3DBF7A 290C3D 57EF1C 577445 2C2ED2 AB6B13
## 0.3333333 0.3333333 9.8244106 17.1075313 0.0000000 0.8333333
## 24CE0B
## 25.5333333hist(betweenness_centrality, bins= 20, labels = TRUE, ylim = c(0,40), xlim = c(0,300), xlab = "Betweenness Centrality", main = "Histograma de los Valores de Betweenness Centrality")## Warning in plot.window(xlim, ylim, "", ...): "bins" is not a graphical parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...): "bins"
## is not a graphical parameter## Warning in axis(1, ...): "bins" is not a graphical parameter## Warning in axis(2, ...): "bins" is not a graphical parameter
which(betweenness_centrality >= 100)## A8E571 535FB1
## 1 25- A8E571: Federal Agents Make Massive Discovery at Southern Border… ISIS Is Here ⋆ Freedom Daily (Fake)
- 535FB1: Barack Obama at Benjamin Netanyahu meeting: ‘He is always very candid with us’ (Real)
Graficamos a continuación la red de noticias-noticias donde cada nodo tiene el tamaño y color asociado a la métrica de betweenness calculada anteriormente.
colorfunc <- colorRampPalette(c( "red",
"firebrick",
"lightcoral",
"saddlebrown",
"tomato",
"darkorange",
"gold",
"lightgreen",
"mediumspringgreen",
"dodgerblue",
"steelblue"))
scaled_betweenness = betweenness_centrality + 1
colors <- colorfunc(max(scaled_betweenness))
V(news_projected)$color <- colors[scaled_betweenness]
V(news_projected)$shape <- "circle"
E(news_projected)$color <- "lightgray"
plot(news_projected,
vertex.label = format(round(betweenness_centrality), nsmall = 1),
vertex.size = betweenness_centrality / 9,
edge.arrow.size=0.1,
layout = layout_nicely,
main = "Proyección Red Noticias: Nodos Según Betweenness Centrality")
plot(news_projected,
vertex.label = NA,
vertex.size = betweenness_centrality / 9,
edge.arrow.size=0.1,
layout = layout_nicely,
main = "Proyección Red Noticias: Nodos Según Betweenness Centrality")
Medidas Sobre Conjuntos de Nodos
Cliques
Cálculo de la distribución de cliques:
count_max_cliques(news_projected)## [1] 26La red contiene 66 cliques.
¿Cuál es el tamaño de cada clique?
total_cliques = max_cliques(news_projected)
cliques_sizes = unlist(lapply(total_cliques, length))
h = hist(cliques_sizes,
ylim=c(0,40),
breaks = 5,
xlab = "Tamaño de los cliques",
ylab = "Frecuencia",
main = "Distribución del tamaño de los cliques" )
text(h$mids,h$counts,labels=h$counts, adj=c(0.5, -0.5))
Graficamos el clique más grande
largestCliques <- largest_cliques(news_projected)
largestClique_1 = largestCliques[[1]]
g_induced = induced_subgraph(news_projected, largestClique_1)
V(g_induced)$color <- "lightblue"
plot(g_induced,
vertex.label = NA,
layout=layout_components,
edge.arrow.size=0.05,
vertex.size = 10,
main = "Clique Más Grande")
Medidas Globales
Radio
Calculado obteniendo el mínimo valor de eccentricity
eccentricity_metric = eccentricity(news_projected)
min(eccentricity_metric)## [1] 3Diametro
diameter_metric = diameter(news_projected)
diameter_metric## [1] 7Average Path Length
avg_path_length = mean_distance(news_projected, directed=F)
avg_path_length## [1] 2.358859Transitivity
clustering_avg = transitivity(news_projected, type = "average")
clustering_avg## [1] 0.7297252Coeficiente de Reciprocidad
reciprocity(news_projected)## [1] 1Subredes
K-Core
La función coreness nos da una tabla indicando que core pertenece a cada nodo:
coreness(news_projected)## A8E571 F466AA 9C8FAF B4EA27 229D27 F6430A 8B4AE4 7EB31E 6513A0 CA534D 19776E
## 5 4 11 11 11 11 5 3 3 1 5
## C2F13C 5C5CB8 232D46 3D02CC 5D1A52 8FFAFB 7E4715 A26558 92F413 3A6ED0 78D9FA
## 11 10 11 5 5 11 5 11 11 5 5
## 581460 633F13 535FB1 719576 C3D240 7253B0 A0E7B2 CCFD5A 3DBF7A 290C3D 57EF1C
## 11 5 11 5 5 5 11 2 3 3 3
## 577445 2C2ED2 AB6B13 24CE0B
## 5 2 2 3En este gráfico podemos observar la red noticia-noticia. Cada nodo tiene un atributo asignado que es el K-core obtenido mediante la función de coreness. Además, cada nodo tiene asociado un color en función del valor del k-core. Los nodos con valores más altos, son los que están más densamente conectados.
kcore <- coreness(news_projected) # Extraemos k-cores
V(news_projected)$core <- kcore # Agregamos los k-cores calculados como atributo de los nodos
colorfunc <- colorRampPalette(c( "Red 2",
"lightcoral",
"tomato",
"orange",
"Cadet Blue 1",
"Yellow 3",
"gold",
"lightgreen",
"Light Steel Blue 2",
"Royal Blue 1",
"Deep Sky Blue 1",
"Slate Blue"))
colors <- colorfunc(max(kcore))
V(news_projected)$color <- colors[kcore]
plot(news_projected,
vertex.label = kcore,
vertex.size = 15,
layout = layout_components,
main = " Nodos Según El core al que Pertenecen")
egog = make_ego_graph(news_projected, order=1)
k_g = egog[[1]]
degree_k_g = degree(k_g)
colorfunc <- colorRampPalette(c( "Red 2",
"lightcoral",
"tomato",
"orange",
"Cadet Blue 1",
"Yellow 3",
"gold",
"lightgreen",
"Light Steel Blue 2",
"Royal Blue 1",
"Deep Sky Blue 1",
"Slate Blue"))
scaled_degree = degree_k_g + 1
colors <- colorfunc(max(scaled_degree))
V(k_g)$color <- colors[scaled_degree]
plot(k_g,
vertex.size = 12,
vertex.label = degree_k_g,
edge.arrow.size=0.3,
layout = layout_components,
main = "Subgrafo Inducido Usuario-Usuario: Nodos Según Degree")
rm(news_projected)Proyección: Red Usuarios
Análisis de los componentes de la red
connected_components = components(users_projected)
connected_components## $membership
## 10 11 41 51 114 67 78 82 85 98 107 128 132
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 133 134 143 144 169 182 187 189 203 231 234 246 254
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 257 258 281 306 345 349 354 376 393 398 448 456 466
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 470 471 478 499 519 521 522 529 533 568 595 620 639
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 648 656 657 679 697 743 771 796 822 837 838 840 841
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 859 861 873 917 940 946 948 968 973 976 994 1000 1002
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1024 1031 1032 1039 1055 1076 1102 1110 1138 1163 1172 1175 1209
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1214 1218 1223 1224 1257 1258 1280 1307 1313 1329 1362 1412 1415
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1416 1424 1429 1445 1462 1467 1481 1483 1494 1507 1544 1559 1596
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1598 1639 1657 1660 1705 1709 1718 1739 1752 1760 1790 1826 1833
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1849 1884 1898 1902 1924 1933 1984 1994 2001 2011 2019 2026 2042
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2063 2084 2129 2143 2149 2188 2192 2193 2199 2227 2250 2286 2293
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2316 2329 2330 2335 2349 2350 2377 2380 2388 2393 2405 2415 2426
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2427 2460 2462 2466 2471 2484 2491 2492 2502 2504 2516 2524 2526
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2527 2533 2542 2549 2557 2562 2582 2596 2601 2603 2623 2660 2663
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2701 2720 2730 2738 2795 2806 2811 2820 2835 2844 2846 2849 2861
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2872 2876 2908 2920 2951 2961 2986 2987 3009 3020 3029 3051 3058
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 3065 3066 3091 3103 3119 3134 3136 3137 3150 3152 3158 3167 3186
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 3187 3200 3230 3260 3265 3278 3298 3314 3327 3345 3350 3356 3384
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 3397 3401 3404 3408 3411 3443 3459 3461 3469 3491 3505 3516 3519
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 3533 3549 3554 3557 3571 3577 3595 3596 3635 3648 3651 3723 3727
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 3728 3733 3750 3752 3753 3790 3833 3845 3857 3873 3893 3940 3965
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 3987 3989 4010 4011 4014 4068 4070 4080 4081 4102 4124 4159 4214
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 4229 4243 4245 4273 4276 4303 4304 4310 4323 4350 4352 4369 4374
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 4384 4432 4461 4470 4472 4476 4489 4526 4536 4542 4550 4590 4600
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 4615 4633 4667 4705 4708 4730 4750 4751 4756 4760 4792 4800 4801
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 4820 4822 4823 4833 4843 4846 4862 4864 4875 4879 4890 4904 4910
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 4921 4930 4954 4964 4982 5014 5015 5044 5049 5055 5057 5072 5103
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 5115 5120 5132 5136 5146 5179 5189 5223 5231 5251 5292 5293 5327
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 5331 5342 5359 5423 5424 5438 5446 5453 5461 5466 5507 5508 5531
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 5547 5552 5554 5556 5564 5579 5583 5597 5624 5666 5670 5688 5707
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 5714 5726 5731 5771 5793 5846 5849 5867 5881 5886 5899 5901 5902
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 5904 5905 5906 5911 5912 5918 5920 5939 5948 5962 5965 5973 5994
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 5996 5999 6003 6009 6018 6022 6054 6078 6085 6118 6121 6125 6134
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 6145 6163 6169 6173 6190 6202 6221 6223 6258 6322 6336 6345 6349
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 6350 6354 6367 6370 6373 6391 6404 6451 6472 6498 6505 6554 6563
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 6576 6591 6600 6630 6647 6679 6690 6695 6742 6757 6764 6784 6785
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 6815 6821 6857 6860 6869 6876 6880 6901 6914 6918 6920 6938 6939
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 6943 6946 6949 6955 6962 7026 7041 7048 7082 7103 7109 7116 7129
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 7137 7194 7202 7242 7261 7277 7295 7313 7316 7335 7337 7345 7352
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 7367 7373 7375 7392 7397 7398 7401 7430 7456 7458 7461 7472 7480
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 7482 7483 7508 7514 7564 7566 7569 7578 7619 7623 7624 7649 7697
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 7718 7731 7746 7756 7762 7765 7772 7779 7825 7830 7837 7850 7859
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 7862 7887 7890 7902 7904 7905 7941 7962 7964 7966 7983 8006 8015
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 8030 8077 8113 8123 8133 8134 8168 8174 8181 8183 8208 8220 8223
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 8253 8267 8293 8294 8309 8338 8342 8362 8365 8370 8373 8382 8393
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 8413 8426 8485 8542 8546 8566 8593 8609 8627 8663 8679 8708 8742
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 8746 8751 8761 8783 8787 8798 8825 8831 8850 8868 8878 8880 8894
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 8914 8931 8939 8953 8957 8992 8997 9015 9018 9025 9048 9061 9095
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 9098 9117 9129 9140 9163 9204 9210 9228 9229 9250 9253 9309 9310
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 9320 9341 9350 9355 9358 9373 9384 9407 9413 9424 9481 9486 9500
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 9553 9568 9570 9596 9607 9619 9641 9652 9659 9661 9664 9679 9682
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 9684 9714 9726 9763 9766 9782 9785 9792 9794 9809 9816 9834 9848
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 9851 9857 9877 9895 9905 9920 10003 10012 10054 10105 10110 10117 10118
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 10130 10155 10178 10201 10206 10209 10212 10241 10262 10270 10279 10290 10303
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 10364 10371 10386 10404 10423 10439 10449 10475 10480 10487 10500 10512 10516
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 10527 10544 10549 10568 10572 10582 10590 10638 10650 10694 10708 10724 10730
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 10736 10759 10771 10774 10776 10782 10802 10808 10836 10862 10874 10882 10886
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 10891 10906 10920 10929 10944 10952 10974 10995 11011 11025 11044 11073 11074
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 11123 11132 11142 11146 11156 11164 11171 11178 11217 11225 11234 11257 11296
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 11302 11305 11342 11352 11388 11390 11396 11417 11443 11456 11457 11461 11504
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 11527 11559 11560 11592 11606 11608 11642 11664 11671 11678 11700 11704 11707
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 11728 11729 11735 11753 11757 11818 11820 11839 11843 11859 11862 11887 11896
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 11935 11980 11988 12016 12030 12032 12049 12051 12075 12084 12091 12109 12114
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 12117 12128 12147 12160 12166 12190 12216 12236 12283 12307 12335 12367 12372
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 12376 12380 12382 12389 12395 12397 12408 12427 12439 12450 12452 12465 12468
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 12490 12496 12522 12527 12528 12537 12546 12550 12552 12554 12561 12568 12570
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 12579 12593 12607 12609 12622 12648 12705 12707 12739 12751 12761 12782 12799
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 12804 12806 12816 12843 12887 12888 12894 12901 12906 12918 12933 12943 12948
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 12956 12964 12968 12980 12984 13000 13035 13052 13087 13092 13104 13111 13133
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 13136 13137 13138 13143 13150 13154 13159 13188 13252 13261 13263 13284 13335
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 13336 13341 13360 13396 13450 13469 13497 13540 13553 13565 13586 13602 13621
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 13635 13636 13647 13656 13660 13674 13675 13682 13690 13732 13737 13742 13753
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 13755 13761 13777 13778 13782 13784 13791 13807 13892 13916 13962 13967 13989
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 14009 14035 14037 14060 14061 14064 14085 14095 14104 14110 14118 14119 14122
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 14125 14135 14137 14158 14187 14203 14204 14210 14216 14221 14256 14305 14311
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 14318 14321 14346 14358 14362 14368 14374 14403 14404 14414 14422 14435 14443
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 14481 14543 14571 14581 14592 14609 14628 14633 14635 14664 14683 14692 14725
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 14741 14743 14747 14751 14762 14780 14781 14787 14817 14819 14860 14895 14907
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 14925 14945 14947 14955 14963 15005 15024 15037 15096 15134 15143 15194 15196
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 15200 15217 15220 15242 15249
## 1 1 1 1 1
##
## $csize
## [1] 1045
##
## $no
## [1] 1connected_components$no## [1] 1connected_components$csize## [1] 1045Medidas de Centralidad Basadas en Primeros Vecinos
Degree Centrality
degree_centrality = degree(users_projected)
sort(degree_centrality)## 2820 3260 4350 5579 9018 9857 10475 14609 85 2806 2872 6918 11164
## 2 2 2 2 2 2 2 2 3 3 3 3 3
## 11461 1826 7762 2533 4352 6914 9607 13916 9782 9895 10118 14571 14781
## 3 4 4 5 5 5 5 5 6 6 6 6 7
## 133 1657 2861 4800 5461 10836 11073 12166 13263 14256 132 456 841
## 11 11 11 11 11 11 11 11 11 11 12 12 12
## 2582 4550 4823 10212 11457 13635 114 595 1362 3461 4080 5132 6202
## 12 12 12 12 12 12 14 14 14 14 14 14 14
## 6939 8627 12816 13469 128 1032 2063 2250 2557 4124 4461 4879 5103
## 14 14 14 14 18 18 18 18 18 18 18 18 18
## 7472 9714 10886 11396 12128 12372 14104 14683 14860 143 743 837 1055
## 18 18 18 18 18 18 18 18 18 21 21 21 21
## 2405 3134 6345 6647 6876 7277 7337 10708 11302 11456 11980 12439 12554
## 21 21 21 21 21 21 21 21 21 21 21 21 21
## 12761 13138 13565 13778 14064 4590 519 3845 6173 9320 11729 12739 14311
## 21 21 21 21 21 24 25 25 25 25 25 25 25
## 41 144 1429 1462 1598 5014 7564 11156 11171 471 948 2466 3137
## 28 28 28 28 28 28 28 28 28 30 30 30 30
## 5886 5905 6373 7295 8393 8783 8787 8957 9061 9481 10512 10771 11044
## 30 30 30 30 30 30 30 30 30 30 30 30 30
## 13150 13636 3356 3733 4374 4751 4756 4875 4964 5597 6145 7825 7862
## 30 30 31 31 31 31 31 31 31 31 31 31 31
## 8030 8708 10110 11388 11843 12051 12307 13737 14216 15242 1718 4930 376
## 31 31 31 31 31 31 31 31 31 31 34 40 44
## 648 1445 2377 2951 3965 4214 4245 4615 5251 5292 6600 7430 7962
## 44 44 44 44 44 44 44 44 44 44 44 44 44
## 8663 9309 9384 10516 10694 11443 12593 14947 838 1031 1483 2199 2460
## 44 44 44 44 44 44 44 44 46 46 46 46 46
## 3491 3987 4904 4954 4982 5359 5424 6022 6349 6857 7458 7746 7859
## 46 46 46 46 46 46 46 46 46 46 46 46 46
## 8168 8253 8914 8939 9413 10279 10724 11352 12117 12382 12622 12843 12918
## 46 46 46 46 46 46 46 46 46 46 46 46 46
## 13660 14443 14633 15196 393 2623 2701 2987 3103 3167 4011 4705 4862
## 46 46 46 46 48 48 48 48 48 48 48 48 48
## 5044 5939 5999 7116 7345 7397 7569 7830 8362 9163 9664 9851 10776
## 48 48 48 48 48 48 48 48 48 48 48 48 48
## 11123 11142 11753 12527 12943 14009 187 306 976 1258 2019 2516 2720
## 48 48 48 48 48 48 52 52 52 52 52 52 52
## 3136 3401 3549 4801 5564 6134 6169 6350 6690 7109 8181 8365 8679
## 52 52 52 52 52 52 52 52 52 52 52 52 52
## 9596 9766 9809 9920 10262 10952 11011 11504 12522 12546 12804 13586 14110
## 52 52 52 52 52 52 52 52 52 52 52 52 52
## 14158 14221 14895 15134 15200 1307 657 9358 182 1002 1313 1494 2349
## 52 52 52 52 52 53 57 61 62 62 62 62 62
## 2491 3020 3635 3753 7902 8174 8746 9048 9553 9652 10874 11592 12283
## 62 62 62 62 62 62 62 62 62 62 62 62 62
## 12389 12395 14061 14581 5136 78 246 697 1639 1739 1760 1884 2011
## 62 62 62 62 64 67 67 67 67 67 67 67 67
## 2335 2393 2524 2961 3404 3411 3577 3595 3940 5146 5189 5446 5899
## 67 67 67 67 67 67 67 67 67 67 67 67 67
## 5948 6336 7137 7649 7850 7890 9407 9570 9641 9792 10423 10480 10582
## 67 67 67 67 67 67 67 67 67 67 67 67 67
## 11178 11305 11678 11820 12468 13154 13284 13335 13497 13621 13656 13690 14118
## 67 67 67 67 67 67 67 67 67 67 67 67 67
## 14955 15220 14204 12933 6078 8370 9679 15217 8267 11 257 345 349
## 67 67 68 70 72 72 72 72 75 81 81 81 81
## 398 499 533 639 796 822 861 873 946 1790 1924 2001 2192
## 81 81 81 81 81 81 81 81 81 81 81 81 81
## 2330 2730 2738 3058 3278 3505 3857 3989 4276 4470 4542 4890 5331
## 81 81 81 81 81 81 81 81 81 81 81 81 81
## 5554 5731 5912 6221 6322 6354 6404 6563 6764 6901 7041 7313 7401
## 81 81 81 81 81 81 81 81 81 81 81 81 81
## 7456 7697 7964 8006 8413 8742 8850 8953 9373 9486 9500 10117 10782
## 81 81 81 81 81 81 81 81 81 81 81 81 81
## 11132 11527 11664 11818 11839 11862 12114 12160 12450 12452 12705 12751 12782
## 81 81 81 81 81 81 81 81 81 81 81 81 81
## 12964 13052 13137 13682 13791 14374 14481 14817 82 968 4068 3873 2526
## 81 81 81 81 81 81 81 81 85 87 87 92 93
## 5055 5507 11728 254 3727 8878 10891 14963 2663 51 203 231 478
## 93 93 93 102 110 110 110 115 120 126 126 126 126
## 521 529 859 1209 1415 1424 1481 1709 1752 1902 2293 2415 2504
## 126 126 126 126 126 126 126 126 126 126 126 126 126
## 2603 2876 2986 3469 3554 3596 3651 3728 3752 4304 4792 4820 4921
## 126 126 126 126 126 126 126 126 126 126 126 126 126
## 5453 5911 6125 6576 6955 7461 7566 7765 7837 7966 8133 8338 8373
## 126 126 126 126 126 126 126 126 126 126 126 126 126
## 8426 8566 8825 8894 8997 9253 9350 10449 10544 10568 10590 10882 10920
## 126 126 126 126 126 126 126 126 126 126 126 126 126
## 11074 11559 11859 12016 12049 12236 12380 12397 12490 12496 13111 13360 13602
## 126 126 126 126 126 126 126 126 126 126 126 126 126
## 13742 13782 14095 14203 14358 14635 14692 15194 5508 11608 9140 2188 2562
## 126 126 126 126 126 126 126 126 129 129 134 135 135
## 3397 8880 9250 1110 4843 6009 10178 12956 3152 10404 12561 8309 10054
## 137 137 137 140 140 140 140 140 141 141 141 143 153
## 11342 1559 7772 13136 15143 3187 522 771 1544 1898 5115 7373 7756
## 153 157 157 157 157 163 169 169 169 169 169 169 169
## 8609 13087 8134 9848 3158 2596 6054 940 5120 15005 10 107 169
## 169 169 170 170 181 196 196 202 207 215 220 220 220
## 234 656 973 1024 1039 1102 1138 1172 1214 1223 1257 1280 1329
## 220 220 220 220 220 220 220 220 220 220 220 220 220
## 1705 1933 1984 1994 2286 2388 2484 2492 2502 2549 2601 2908 2920
## 220 220 220 220 220 220 220 220 220 220 220 220 220
## 3051 3066 3230 3350 3533 3571 3723 3750 3790 3893 4102 4159 4303
## 220 220 220 220 220 220 220 220 220 220 220 220 220
## 4310 4472 4476 4536 4600 4708 4750 4822 4833 4846 5015 5179 5231
## 220 220 220 220 220 220 220 220 220 220 220 220 220
## 5293 5327 5547 5624 5666 5670 5688 5707 5793 5867 5881 5901 5902
## 220 220 220 220 220 220 220 220 220 220 220 220 220
## 5904 5918 5994 6003 6085 6190 6367 6370 6391 6472 6498 6679 6695
## 220 220 220 220 220 220 220 220 220 220 220 220 220
## 6785 6938 6943 6946 7194 7261 7316 7335 7352 7398 7482 7508 7578
## 220 220 220 220 220 220 220 220 220 220 220 220 220
## 7718 7731 7887 7905 7941 7983 8015 8077 8113 8183 8208 8293 8342
## 220 220 220 220 220 220 220 220 220 220 220 220 220
## 8382 8542 8761 8798 8831 8931 9095 9117 9228 9229 9424 9568 9619
## 220 220 220 220 220 220 220 220 220 220 220 220 220
## 9684 9816 9834 9905 10003 10012 10290 10303 10439 10487 10549 10572 10736
## 220 220 220 220 220 220 220 220 220 220 220 220 220
## 10808 10995 11025 11217 11296 11417 11560 11671 11896 12032 12075 12084 12190
## 220 220 220 220 220 220 220 220 220 220 220 220 220
## 12367 12376 12427 12550 12552 12568 12609 12648 12894 12980 13092 13104 13133
## 220 220 220 220 220 220 220 220 220 220 220 220 220
## 13159 13261 13341 13553 13755 13761 13784 13967 13989 14037 14060 14137 14187
## 220 220 220 220 220 220 220 220 220 220 220 220 220
## 14321 14362 14368 14403 14414 14725 14741 14747 14780 14819 14925 15024 15037
## 220 220 220 220 220 220 220 220 220 220 220 220 220
## 14346 10105 13540 568 2227 3519 14119 11887 67 98 189 281 354
## 221 231 231 246 246 246 246 248 253 253 253 253 253
## 448 470 620 840 917 994 1000 1076 1218 1224 1412 1416 1507
## 253 253 253 253 253 253 253 253 253 253 253 253 253
## 1596 1660 1849 2026 2084 2129 2143 2149 2193 2350 2380 2462 2471
## 253 253 253 253 253 253 253 253 253 253 253 253 253
## 2527 2542 2795 2811 2835 2844 2849 3029 3065 3091 3119 3150 3186
## 253 253 253 253 253 253 253 253 253 253 253 253 253
## 3298 3314 3327 3345 3384 3408 3443 3459 3557 3648 3833 4014 4070
## 253 253 253 253 253 253 253 253 253 253 253 253 253
## 4081 4229 4243 4273 4323 4369 4432 4526 4633 4667 4760 4864 4910
## 253 253 253 253 253 253 253 253 253 253 253 253 253
## 5057 5072 5223 5342 5423 5438 5531 5556 5583 5726 5771 5849 5906
## 253 253 253 253 253 253 253 253 253 253 253 253 253
## 5965 5973 6118 6121 6163 6223 6258 6451 6505 6554 6742 6757 6821
## 253 253 253 253 253 253 253 253 253 253 253 253 253
## 6860 6920 6949 7026 7048 7082 7103 7129 7202 7242 7367 7375 7392
## 253 253 253 253 253 253 253 253 253 253 253 253 253
## 7480 7514 7619 7623 7779 7904 8220 8223 8294 8485 8546 8593 8868
## 253 253 253 253 253 253 253 253 253 253 253 253 253
## 9015 9025 9204 9210 9310 9341 9355 9661 9682 9785 9794 9877 10130
## 253 253 253 253 253 253 253 253 253 253 253 253 253
## 10155 10201 10209 10270 10371 10500 10527 10730 10759 10774 10802 10862 10906
## 253 253 253 253 253 253 253 253 253 253 253 253 253
## 10929 10944 11146 11225 11257 11390 11606 11642 11700 11704 11757 11935 11988
## 253 253 253 253 253 253 253 253 253 253 253 253 253
## 12030 12091 12408 12465 12528 12537 12570 12579 12607 12799 12806 12887 12888
## 253 253 253 253 253 253 253 253 253 253 253 253 253
## 12906 12968 12984 13035 13143 13188 13252 13336 13396 13450 13647 13674 13675
## 253 253 253 253 253 253 253 253 253 253 253 253 253
## 13732 13753 13807 13892 13962 14035 14085 14122 14125 14135 14210 14305 14318
## 253 253 253 253 253 253 253 253 253 253 253 253 253
## 14422 14435 14543 14592 14628 14664 14743 14751 14787 14907 14945 15096 15249
## 253 253 253 253 253 253 253 253 253 253 253 253 253
## 258 4489 5962 6591 6784 6962 10364 466 5552 8751 1163 6880 12901
## 257 257 257 257 257 257 257 260 260 260 265 265 265
## 10206 5466 7483 10650 4384 6018 7624 10386 10638 134 679 1175 1467
## 268 269 275 276 277 277 277 279 280 284 284 284 284
## 2316 2329 2660 2846 3009 4010 4730 5049 5714 5846 5920 6869 8123
## 284 284 284 284 284 284 284 284 284 284 284 284 284
## 8992 10974 11735 12147 12216 12335 12707 13000 14762 2042 3265 9659 12109
## 284 284 284 284 284 284 284 284 284 292 305 305 306
## 3200 9726 9763 12948 13777 9129 10241 1833 6630 9098 11707 3516 2426
## 308 316 316 316 321 349 349 369 369 369 369 393 395
## 6815 11234 2427 5996 14404
## 402 427 428 432 476max(degree_centrality)## [1] 476hist(unname(degree_centrality),labels = TRUE, xlim = c(0,600), ylim = c(0,350), xlab = "Degree", main = "Histograma de los Degree")
A continuación graficamos la proyección de la red bipartita en función de los usuarios. Cada nodo tiene asignado un color que coincide con el degree del mismo:
colorfunc <- colorRampPalette(c( "Medium Slate Blue",
"Magenta",
"Maroon 2",
"Purple",
"red",
"firebrick",
"lightcoral",
"saddlebrown",
"tomato",
"darkorange",
"gold",
"lightgreen",
"mediumspringgreen",
"dodgerblue",
"steelblue",
"Light Steel Blue 1",
"Royal Blue 2"))
colors <- colorfunc(max(degree_centrality))
V(users_projected)$color <- colors[degree_centrality]
V(users_projected)$label <- degree_centrality
V(users_projected)$shape <- "circle"
E(users_projected)$color <- "lightgray"
plot(users_projected,
vertex.size = 2,
edge.arrow.size=0.1,
layout=layout_components,
main = "Proyección Red Usuarios: Nodos Según Degree")
plot(users_projected,
vertex.size = 4,
vertex.label = NA,
edge.arrow.size=0.1,
layout=layout_components,
main = "Proyección Red Usuarios: Nodos Según Degree")
Eigenvector Centrality
Esta métrica nos da una intuición de la influencia que un nodo tiene en la red. Si un nodo es alcanzado por muchos otros nodos (que además tienen una alta eigenvector centrality) ese nodo va a tener un eigenvector centrality mayor.
Podríamos decir que eigenvector centrality calcula el acceso indirecto al poder de un determinado nodo.
eigenvector_cetrality <- evcent(users_projected)$vector
eigenvector_cetrality## 10 11 41 51 114 67
## 1.851816e-04 1.283902e-03 3.094893e-02 8.309411e-02 8.051461e-03 7.473835e-01
## 78 82 85 98 107 128
## 1.361263e-05 1.320908e-03 3.240013e-04 7.473835e-01 1.851816e-04 5.317688e-05
## 132 133 134 143 144 169
## 1.632376e-06 8.568722e-03 8.693217e-01 1.384216e-02 3.094893e-02 1.851816e-04
## 182 187 189 203 231 234
## 1.252681e-01 7.865246e-06 7.473835e-01 8.309411e-02 8.309411e-02 1.851816e-04
## 246 254 257 258 281 306
## 1.361263e-05 1.506834e-02 1.283902e-03 1.955626e-04 7.473835e-01 7.865246e-06
## 345 349 354 376 393 398
## 1.283902e-03 1.283902e-03 7.473835e-01 3.124682e-02 1.113006e-05 1.283902e-03
## 448 456 466 470 471 478
## 7.473835e-01 1.632376e-06 2.210498e-04 7.473835e-01 1.292210e-02 8.309411e-02
## 499 519 521 522 529 533
## 1.283902e-03 2.829945e-02 8.309411e-02 1.716172e-01 8.309411e-02 1.283902e-03
## 568 595 620 639 648 656
## 2.177740e-04 3.192423e-06 7.473835e-01 1.283902e-03 3.124682e-02 1.851816e-04
## 657 679 697 743 771 796
## 3.914832e-02 8.693217e-01 1.361263e-05 1.384216e-02 1.716172e-01 1.283902e-03
## 822 837 838 840 841 859
## 1.283902e-03 1.384216e-02 3.671491e-05 7.473835e-01 1.632376e-06 8.309411e-02
## 861 873 917 940 946 948
## 1.283902e-03 1.283902e-03 7.473835e-01 2.196020e-01 1.283902e-03 1.292210e-02
## 968 973 976 994 1000 1002
## 1.376629e-01 1.851816e-04 7.865246e-06 7.473835e-01 7.473835e-01 1.252681e-01
## 1024 1031 1032 1039 1055 1076
## 1.851816e-04 3.671491e-05 5.317688e-05 1.851816e-04 1.384216e-02 7.473835e-01
## 1102 1110 1138 1163 1172 1175
## 1.851816e-04 9.947152e-02 1.851816e-04 1.923103e-04 1.851816e-04 8.693217e-01
## 1209 1214 1218 1223 1224 1257
## 8.309411e-02 1.851816e-04 7.473835e-01 1.851816e-04 7.473835e-01 1.851816e-04
## 1258 1280 1307 1313 1329 1362
## 7.865246e-06 1.851816e-04 1.147608e-05 1.252681e-01 1.851816e-04 8.051461e-03
## 1412 1415 1416 1424 1429 1445
## 7.473835e-01 8.309411e-02 7.473835e-01 8.309411e-02 3.094893e-02 3.124682e-02
## 1462 1467 1481 1483 1494 1507
## 3.094893e-02 8.693217e-01 8.309411e-02 3.671491e-05 1.252681e-01 7.473835e-01
## 1544 1559 1596 1598 1639 1657
## 1.716172e-01 1.016310e-01 7.473835e-01 3.094893e-02 1.361263e-05 8.568722e-03
## 1660 1705 1709 1718 1739 1752
## 7.473835e-01 1.851816e-04 8.309411e-02 5.977311e-06 1.361263e-05 8.309411e-02
## 1760 1790 1826 1833 1849 1884
## 1.361263e-05 1.283902e-03 4.206519e-05 8.273087e-01 7.473835e-01 1.361263e-05
## 1898 1902 1924 1933 1984 1994
## 1.716172e-01 8.309411e-02 1.283902e-03 1.851816e-04 1.851816e-04 1.851816e-04
## 2001 2011 2019 2026 2042 2063
## 1.283902e-03 1.361263e-05 7.865246e-06 7.473835e-01 6.573193e-03 5.317688e-05
## 2084 2129 2143 2149 2188 2192
## 7.473835e-01 7.473835e-01 7.473835e-01 7.473835e-01 9.183034e-02 1.283902e-03
## 2193 2199 2227 2250 2286 2293
## 7.473835e-01 3.671491e-05 2.177740e-04 5.317688e-05 1.851816e-04 8.309411e-02
## 2316 2329 2330 2335 2349 2350
## 8.693217e-01 8.693217e-01 1.283902e-03 1.361263e-05 1.252681e-01 7.473835e-01
## 2377 2380 2388 2393 2405 2415
## 3.124682e-02 7.473835e-01 1.851816e-04 1.361263e-05 1.384216e-02 8.309411e-02
## 2426 2427 2460 2462 2466 2471
## 9.484796e-01 9.216813e-01 3.671491e-05 7.473835e-01 1.292210e-02 7.473835e-01
## 2484 2491 2492 2502 2504 2516
## 1.851816e-04 1.252681e-01 1.851816e-04 1.851816e-04 8.309411e-02 7.865246e-06
## 2524 2526 2527 2533 2542 2549
## 1.361263e-05 1.892283e-05 7.473835e-01 3.899759e-07 7.473835e-01 1.851816e-04
## 2557 2562 2582 2596 2601 2603
## 5.317688e-05 9.183034e-02 1.632376e-06 2.205150e-01 1.851816e-04 8.309411e-02
## 2623 2660 2663 2701 2720 2730
## 1.113006e-05 8.693217e-01 1.951956e-01 1.113006e-05 7.865246e-06 1.283902e-03
## 2738 2795 2806 2811 2820 2835
## 1.283902e-03 7.473835e-01 6.423139e-04 7.473835e-01 1.534919e-06 7.473835e-01
## 2844 2846 2849 2861 2872 2876
## 7.473835e-01 8.693217e-01 7.473835e-01 8.568722e-03 4.415451e-04 8.309411e-02
## 2908 2920 2951 2961 2986 2987
## 1.851816e-04 1.851816e-04 3.124682e-02 1.361263e-05 8.309411e-02 1.113006e-05
## 3009 3020 3029 3051 3058 3065
## 8.693217e-01 1.252681e-01 7.473835e-01 1.851816e-04 1.283902e-03 7.473835e-01
## 3066 3091 3103 3119 3134 3136
## 1.851816e-04 7.473835e-01 1.113006e-05 7.473835e-01 1.384216e-02 7.865246e-06
## 3137 3150 3152 3158 3167 3186
## 1.292210e-02 7.473835e-01 8.318815e-05 2.075671e-01 1.113006e-05 7.473835e-01
## 3187 3200 3230 3260 3265 3278
## 1.441853e-01 8.788411e-01 1.851816e-04 3.859943e-03 8.967309e-01 1.283902e-03
## 3298 3314 3327 3345 3350 3356
## 7.473835e-01 7.473835e-01 7.473835e-01 7.473835e-01 1.851816e-04 3.342656e-05
## 3384 3397 3401 3404 3408 3411
## 7.473835e-01 9.679743e-02 7.865246e-06 1.361263e-05 7.473835e-01 1.361263e-05
## 3443 3459 3461 3469 3491 3505
## 7.473835e-01 7.473835e-01 3.192423e-06 8.309411e-02 3.671491e-05 1.283902e-03
## 3516 3519 3533 3549 3554 3557
## 8.551731e-01 2.177740e-04 1.851816e-04 7.865246e-06 8.309411e-02 7.473835e-01
## 3571 3577 3595 3596 3635 3648
## 1.851816e-04 1.361263e-05 1.361263e-05 8.309411e-02 1.252681e-01 7.473835e-01
## 3651 3723 3727 3728 3733 3750
## 8.309411e-02 1.851816e-04 2.464828e-05 8.309411e-02 3.342656e-05 1.851816e-04
## 3752 3753 3790 3833 3845 3857
## 8.309411e-02 1.252681e-01 1.851816e-04 7.473835e-01 2.829945e-02 1.283902e-03
## 3873 3893 3940 3965 3987 3989
## 9.815029e-03 1.851816e-04 1.361263e-05 3.124682e-02 3.671491e-05 1.283902e-03
## 4010 4011 4014 4068 4070 4080
## 8.693217e-01 1.113006e-05 7.473835e-01 1.376629e-01 7.473835e-01 3.192423e-06
## 4081 4102 4124 4159 4214 4229
## 7.473835e-01 1.851816e-04 5.317688e-05 1.851816e-04 3.124682e-02 7.473835e-01
## 4243 4245 4273 4276 4303 4304
## 7.473835e-01 3.124682e-02 7.473835e-01 1.283902e-03 1.851816e-04 8.309411e-02
## 4310 4323 4350 4352 4369 4374
## 1.851816e-04 7.473835e-01 3.859943e-03 3.899759e-07 7.473835e-01 3.342656e-05
## 4384 4432 4461 4470 4472 4476
## 1.980357e-04 7.473835e-01 5.317688e-05 1.283902e-03 1.851816e-04 1.851816e-04
## 4489 4526 4536 4542 4550 4590
## 1.955626e-04 7.473835e-01 1.851816e-04 1.283902e-03 1.632376e-06 1.012641e-02
## 4600 4615 4633 4667 4705 4708
## 1.851816e-04 3.124682e-02 7.473835e-01 7.473835e-01 1.113006e-05 1.851816e-04
## 4730 4750 4751 4756 4760 4792
## 8.693217e-01 1.851816e-04 3.342656e-05 3.342656e-05 7.473835e-01 8.309411e-02
## 4800 4801 4820 4822 4823 4833
## 6.393403e-03 7.865246e-06 8.309411e-02 1.851816e-04 1.632376e-06 1.851816e-04
## 4843 4846 4862 4864 4875 4879
## 9.947152e-02 1.851816e-04 1.113006e-05 7.473835e-01 3.342656e-05 5.317688e-05
## 4890 4904 4910 4921 4930 4954
## 1.283902e-03 3.671491e-05 7.473835e-01 8.309411e-02 2.089353e-02 3.671491e-05
## 4964 4982 5014 5015 5044 5049
## 3.342656e-05 3.671491e-05 3.094893e-02 1.851816e-04 1.113006e-05 8.693217e-01
## 5055 5057 5072 5103 5115 5120
## 1.892283e-05 7.473835e-01 7.473835e-01 5.317688e-05 1.716172e-01 8.405604e-02
## 5132 5136 5146 5179 5189 5223
## 3.192423e-06 8.980721e-02 1.361263e-05 1.851816e-04 1.361263e-05 7.473835e-01
## 5231 5251 5292 5293 5327 5331
## 1.851816e-04 3.124682e-02 3.124682e-02 1.851816e-04 1.851816e-04 1.283902e-03
## 5342 5359 5423 5424 5438 5446
## 7.473835e-01 3.671491e-05 7.473835e-01 3.671491e-05 7.473835e-01 1.361263e-05
## 5453 5461 5466 5507 5508 5531
## 8.309411e-02 6.393403e-03 1.964649e-04 1.892283e-05 8.385015e-02 7.473835e-01
## 5547 5552 5554 5556 5564 5579
## 1.851816e-04 2.210498e-04 1.283902e-03 7.473835e-01 7.865246e-06 8.406202e-07
## 5583 5597 5624 5666 5670 5688
## 7.473835e-01 3.342656e-05 1.851816e-04 1.851816e-04 1.851816e-04 1.851816e-04
## 5707 5714 5726 5731 5771 5793
## 1.851816e-04 8.693217e-01 7.473835e-01 1.283902e-03 7.473835e-01 1.851816e-04
## 5846 5849 5867 5881 5886 5899
## 8.693217e-01 7.473835e-01 1.851816e-04 1.851816e-04 1.292210e-02 1.361263e-05
## 5901 5902 5904 5905 5906 5911
## 1.851816e-04 1.851816e-04 1.851816e-04 1.292210e-02 7.473835e-01 8.309411e-02
## 5912 5918 5920 5939 5948 5962
## 1.283902e-03 1.851816e-04 8.693217e-01 1.113006e-05 1.361263e-05 1.955626e-04
## 5965 5973 5994 5996 5999 6003
## 7.473835e-01 7.473835e-01 1.851816e-04 9.221897e-01 1.113006e-05 1.851816e-04
## 6009 6018 6022 6054 6078 6085
## 9.947152e-02 1.980357e-04 3.671491e-05 1.994648e-01 6.987382e-05 1.851816e-04
## 6118 6121 6125 6134 6145 6163
## 7.473835e-01 7.473835e-01 8.309411e-02 7.865246e-06 3.342656e-05 7.473835e-01
## 6169 6173 6190 6202 6221 6223
## 7.865246e-06 2.829945e-02 1.851816e-04 3.192423e-06 1.283902e-03 7.473835e-01
## 6258 6322 6336 6345 6349 6350
## 7.473835e-01 1.283902e-03 1.361263e-05 9.087979e-03 3.671491e-05 7.865246e-06
## 6354 6367 6370 6373 6391 6404
## 1.283902e-03 1.851816e-04 1.851816e-04 1.292210e-02 1.851816e-04 1.283902e-03
## 6451 6472 6498 6505 6554 6563
## 7.473835e-01 1.851816e-04 1.851816e-04 7.473835e-01 7.473835e-01 1.283902e-03
## 6576 6591 6600 6630 6647 6679
## 8.309411e-02 1.955626e-04 3.124682e-02 8.273087e-01 9.087979e-03 1.851816e-04
## 6690 6695 6742 6757 6764 6784
## 7.865246e-06 1.851816e-04 7.473835e-01 7.473835e-01 1.283902e-03 1.955626e-04
## 6785 6815 6821 6857 6860 6869
## 1.851816e-04 9.728658e-01 7.473835e-01 3.671491e-05 7.473835e-01 8.693217e-01
## 6876 6880 6901 6914 6918 6920
## 9.087979e-03 1.923103e-04 1.283902e-03 1.077222e-03 7.570669e-08 7.473835e-01
## 6938 6939 6943 6946 6949 6955
## 1.851816e-04 8.051461e-03 1.851816e-04 1.851816e-04 7.473835e-01 8.309411e-02
## 6962 7026 7041 7048 7082 7103
## 1.955626e-04 7.473835e-01 1.283902e-03 7.473835e-01 7.473835e-01 7.473835e-01
## 7109 7116 7129 7137 7194 7202
## 7.865246e-06 1.113006e-05 7.473835e-01 1.361263e-05 1.851816e-04 7.473835e-01
## 7242 7261 7277 7295 7313 7316
## 7.473835e-01 1.851816e-04 9.087979e-03 1.292210e-02 1.283902e-03 1.851816e-04
## 7335 7337 7345 7352 7367 7373
## 1.851816e-04 1.384216e-02 1.113006e-05 1.851816e-04 7.473835e-01 1.716172e-01
## 7375 7392 7397 7398 7401 7430
## 7.473835e-01 7.473835e-01 1.113006e-05 1.851816e-04 1.283902e-03 3.124682e-02
## 7456 7458 7461 7472 7480 7482
## 1.283902e-03 3.671491e-05 8.309411e-02 5.317688e-05 7.473835e-01 1.851816e-04
## 7483 7508 7514 7564 7566 7569
## 1.947474e-04 1.851816e-04 7.473835e-01 3.094893e-02 8.309411e-02 1.292577e-02
## 7578 7619 7623 7624 7649 7697
## 1.851816e-04 7.473835e-01 7.473835e-01 1.980357e-04 1.361263e-05 1.283902e-03
## 7718 7731 7746 7756 7762 7765
## 1.851816e-04 1.851816e-04 3.671491e-05 1.716172e-01 4.206519e-05 8.309411e-02
## 7772 7779 7825 7830 7837 7850
## 1.415560e-01 7.473835e-01 3.342656e-05 1.113006e-05 8.309411e-02 1.361263e-05
## 7859 7862 7887 7890 7902 7904
## 3.671491e-05 3.342656e-05 1.851816e-04 1.361263e-05 1.252681e-01 7.473835e-01
## 7905 7941 7962 7964 7966 7983
## 1.851816e-04 1.851816e-04 3.124682e-02 1.283902e-03 8.309411e-02 1.851816e-04
## 8006 8015 8030 8077 8113 8123
## 1.283902e-03 1.851816e-04 3.342656e-05 1.851816e-04 1.851816e-04 8.693217e-01
## 8133 8134 8168 8174 8181 8183
## 8.309411e-02 1.177450e-01 3.671491e-05 1.252681e-01 7.865246e-06 1.851816e-04
## 8208 8220 8223 8253 8267 8293
## 1.851816e-04 7.473835e-01 7.473835e-01 3.671491e-05 1.259357e-05 1.851816e-04
## 8294 8309 8338 8342 8362 8365
## 7.473835e-01 1.260691e-01 8.309411e-02 1.851816e-04 1.113006e-05 7.865246e-06
## 8370 8373 8382 8393 8413 8426
## 6.987382e-05 8.309411e-02 1.851816e-04 1.292210e-02 1.283902e-03 8.309411e-02
## 8485 8542 8546 8566 8593 8609
## 7.473835e-01 1.851816e-04 7.473835e-01 8.309411e-02 7.473835e-01 1.716172e-01
## 8627 8663 8679 8708 8742 8746
## 3.192423e-06 3.124682e-02 7.865246e-06 3.342656e-05 1.283902e-03 1.252681e-01
## 8751 8761 8783 8787 8798 8825
## 2.210498e-04 1.851816e-04 1.292210e-02 1.292210e-02 1.851816e-04 8.309411e-02
## 8831 8850 8868 8878 8880 8894
## 1.851816e-04 1.283902e-03 7.473835e-01 2.464828e-05 9.255031e-02 8.309411e-02
## 8914 8931 8939 8953 8957 8992
## 3.671491e-05 1.851816e-04 3.671491e-05 1.283902e-03 1.292210e-02 8.693217e-01
## 8997 9015 9018 9025 9048 9061
## 8.309411e-02 7.473835e-01 1.534919e-06 7.473835e-01 1.252681e-01 1.292210e-02
## 9095 9098 9117 9129 9140 9163
## 1.851816e-04 8.273087e-01 1.851816e-04 2.153154e-04 9.131307e-02 1.113006e-05
## 9204 9210 9228 9229 9250 9253
## 7.473835e-01 7.473835e-01 1.851816e-04 1.851816e-04 9.255031e-02 8.309411e-02
## 9309 9310 9320 9341 9350 9355
## 3.124682e-02 7.473835e-01 2.829945e-02 7.473835e-01 8.309411e-02 7.473835e-01
## 9358 9373 9384 9407 9413 9424
## 1.426783e-05 1.283902e-03 3.124682e-02 1.361263e-05 3.671491e-05 1.851816e-04
## 9481 9486 9500 9553 9568 9570
## 1.292210e-02 1.283902e-03 1.283902e-03 1.252681e-01 1.851816e-04 1.361263e-05
## 9596 9607 9619 9641 9652 9659
## 7.865246e-06 3.899759e-07 1.851816e-04 1.361263e-05 1.252681e-01 8.967309e-01
## 9661 9664 9679 9682 9684 9714
## 7.473835e-01 1.113006e-05 6.987382e-05 7.473835e-01 1.851816e-04 5.317688e-05
## 9726 9763 9766 9782 9785 9792
## 2.337187e-04 2.337187e-04 7.865246e-06 8.048715e-03 7.473835e-01 1.361263e-05
## 9794 9809 9816 9834 9848 9851
## 7.473835e-01 7.865246e-06 1.851816e-04 1.851816e-04 1.177450e-01 1.113006e-05
## 9857 9877 9895 9905 9920 10003
## 8.441843e-07 7.473835e-01 8.048715e-03 1.851816e-04 7.865246e-06 1.851816e-04
## 10012 10054 10105 10110 10117 10118
## 1.851816e-04 1.139098e-01 1.876553e-04 3.342656e-05 1.283902e-03 8.048715e-03
## 10130 10155 10178 10201 10206 10209
## 7.473835e-01 7.473835e-01 9.947152e-02 7.473835e-01 1.964392e-04 7.473835e-01
## 10212 10241 10262 10270 10279 10290
## 1.632376e-06 2.153154e-04 7.865246e-06 7.473835e-01 3.671491e-05 1.851816e-04
## 10303 10364 10371 10386 10404 10423
## 1.851816e-04 1.955626e-04 7.473835e-01 1.980591e-04 9.656638e-02 1.361263e-05
## 10439 10449 10475 10480 10487 10500
## 1.851816e-04 8.309411e-02 7.784107e-07 1.361263e-05 1.851816e-04 7.473835e-01
## 10512 10516 10527 10544 10549 10568
## 1.292210e-02 3.124682e-02 7.473835e-01 8.309411e-02 1.851816e-04 8.309411e-02
## 10572 10582 10590 10638 10650 10694
## 1.851816e-04 1.361263e-05 8.309411e-02 5.188210e-04 7.753625e-01 3.124682e-02
## 10708 10724 10730 10736 10759 10771
## 1.384216e-02 3.671491e-05 7.473835e-01 1.851816e-04 7.473835e-01 1.292210e-02
## 10774 10776 10782 10802 10808 10836
## 7.473835e-01 1.113006e-05 1.283902e-03 7.473835e-01 1.851816e-04 8.568722e-03
## 10862 10874 10882 10886 10891 10906
## 7.473835e-01 1.252681e-01 8.309411e-02 5.317688e-05 2.464828e-05 7.473835e-01
## 10920 10929 10944 10952 10974 10995
## 8.309411e-02 7.473835e-01 7.473835e-01 7.865246e-06 8.693217e-01 1.851816e-04
## 11011 11025 11044 11073 11074 11123
## 7.865246e-06 1.851816e-04 1.292210e-02 6.393403e-03 8.309411e-02 1.113006e-05
## 11132 11142 11146 11156 11164 11171
## 1.283902e-03 1.113006e-05 7.473835e-01 3.094893e-02 4.415451e-04 3.094893e-02
## 11178 11217 11225 11234 11257 11296
## 1.361263e-05 1.851816e-04 7.473835e-01 1.000000e+00 7.473835e-01 1.851816e-04
## 11302 11305 11342 11352 11388 11390
## 1.384216e-02 1.361263e-05 3.236004e-05 3.671491e-05 3.342656e-05 7.473835e-01
## 11396 11417 11443 11456 11457 11461
## 5.317688e-05 1.851816e-04 3.124682e-02 9.087979e-03 1.632376e-06 7.570669e-08
## 11504 11527 11559 11560 11592 11606
## 7.865246e-06 1.283902e-03 8.309411e-02 1.851816e-04 1.252681e-01 7.473835e-01
## 11608 11642 11664 11671 11678 11700
## 8.310226e-02 7.473835e-01 1.283902e-03 1.851816e-04 1.361263e-05 7.473835e-01
## 11704 11707 11728 11729 11735 11753
## 7.473835e-01 8.273087e-01 1.892283e-05 2.829945e-02 8.693217e-01 1.113006e-05
## 11757 11818 11820 11839 11843 11859
## 7.473835e-01 1.283902e-03 1.361263e-05 1.283902e-03 3.342656e-05 8.309411e-02
## 11862 11887 11896 11935 11980 11988
## 1.283902e-03 2.177805e-04 1.851816e-04 7.473835e-01 1.384216e-02 7.473835e-01
## 12016 12030 12032 12049 12051 12075
## 8.309411e-02 7.473835e-01 1.851816e-04 8.309411e-02 3.342656e-05 1.851816e-04
## 12084 12091 12109 12114 12117 12128
## 1.851816e-04 7.473835e-01 2.050135e-04 1.283902e-03 3.671491e-05 5.317688e-05
## 12147 12160 12166 12190 12216 12236
## 8.693217e-01 1.283902e-03 8.568722e-03 1.851816e-04 8.693217e-01 8.309411e-02
## 12283 12307 12335 12367 12372 12376
## 1.252681e-01 3.342656e-05 8.693217e-01 1.851816e-04 5.317688e-05 1.851816e-04
## 12380 12382 12389 12395 12397 12408
## 8.309411e-02 3.671491e-05 1.252681e-01 1.252681e-01 8.309411e-02 7.473835e-01
## 12427 12439 12450 12452 12465 12468
## 1.851816e-04 9.087979e-03 1.283902e-03 1.283902e-03 7.473835e-01 1.361263e-05
## 12490 12496 12522 12527 12528 12537
## 8.309411e-02 8.309411e-02 7.865246e-06 1.113006e-05 7.473835e-01 7.473835e-01
## 12546 12550 12552 12554 12561 12568
## 7.865246e-06 1.851816e-04 1.851816e-04 1.384216e-02 9.656638e-02 1.851816e-04
## 12570 12579 12593 12607 12609 12622
## 7.473835e-01 7.473835e-01 3.124682e-02 7.473835e-01 1.851816e-04 3.671491e-05
## 12648 12705 12707 12739 12751 12761
## 1.851816e-04 1.283902e-03 8.693217e-01 2.829945e-02 1.283902e-03 1.490503e-02
## 12782 12799 12804 12806 12816 12843
## 1.283902e-03 7.473835e-01 7.865246e-06 7.473835e-01 3.192423e-06 3.671491e-05
## 12887 12888 12894 12901 12906 12918
## 7.473835e-01 7.473835e-01 1.851816e-04 1.923103e-04 7.473835e-01 3.671491e-05
## 12933 12943 12948 12956 12964 12968
## 1.363611e-05 1.113006e-05 2.337187e-04 9.947152e-02 1.283902e-03 7.473835e-01
## 12980 12984 13000 13035 13052 13087
## 1.851816e-04 7.473835e-01 8.693217e-01 7.473835e-01 1.283902e-03 1.716172e-01
## 13092 13104 13111 13133 13136 13137
## 1.851816e-04 1.851816e-04 8.309411e-02 1.851816e-04 1.415560e-01 1.283902e-03
## 13138 13143 13150 13154 13159 13188
## 1.384216e-02 7.473835e-01 1.292210e-02 1.361263e-05 1.851816e-04 7.473835e-01
## 13252 13261 13263 13284 13335 13336
## 7.473835e-01 1.851816e-04 6.393403e-03 1.361263e-05 1.361263e-05 7.473835e-01
## 13341 13360 13396 13450 13469 13497
## 1.851816e-04 8.309411e-02 7.473835e-01 7.473835e-01 3.192423e-06 1.361263e-05
## 13540 13553 13565 13586 13602 13621
## 1.876553e-04 1.851816e-04 1.384216e-02 7.865246e-06 8.309411e-02 1.361263e-05
## 13635 13636 13647 13656 13660 13674
## 1.632376e-06 1.292210e-02 7.473835e-01 1.361263e-05 3.671491e-05 7.473835e-01
## 13675 13682 13690 13732 13737 13742
## 7.473835e-01 1.283902e-03 1.361263e-05 7.473835e-01 3.342656e-05 8.309411e-02
## 13753 13755 13761 13777 13778 13782
## 7.473835e-01 1.851816e-04 1.851816e-04 2.051463e-04 1.384216e-02 8.309411e-02
## 13784 13791 13807 13892 13916 13962
## 1.851816e-04 1.283902e-03 7.473835e-01 7.473835e-01 1.077222e-03 7.473835e-01
## 13967 13989 14009 14035 14037 14060
## 1.851816e-04 1.851816e-04 1.113006e-05 7.473835e-01 1.851816e-04 1.851816e-04
## 14061 14064 14085 14095 14104 14110
## 1.252681e-01 9.087979e-03 7.473835e-01 8.309411e-02 5.317688e-05 7.865246e-06
## 14118 14119 14122 14125 14135 14137
## 1.361263e-05 2.177740e-04 7.473835e-01 7.473835e-01 7.473835e-01 1.851816e-04
## 14158 14187 14203 14204 14210 14216
## 7.865246e-06 1.851816e-04 8.309411e-02 1.440165e-05 7.473835e-01 3.342656e-05
## 14221 14256 14305 14311 14318 14321
## 7.865246e-06 8.568722e-03 7.473835e-01 9.095206e-03 7.473835e-01 1.851816e-04
## 14346 14358 14362 14368 14374 14403
## 2.402165e-01 8.309411e-02 1.851816e-04 1.851816e-04 1.283902e-03 1.851816e-04
## 14404 14414 14422 14435 14443 14481
## 9.479464e-01 1.851816e-04 7.473835e-01 7.473835e-01 3.671491e-05 1.283902e-03
## 14543 14571 14581 14592 14609 14628
## 7.473835e-01 8.048715e-03 1.252681e-01 7.473835e-01 8.406202e-07 7.473835e-01
## 14633 14635 14664 14683 14692 14725
## 3.671491e-05 8.309411e-02 7.473835e-01 5.317688e-05 8.309411e-02 1.851816e-04
## 14741 14743 14747 14751 14762 14780
## 1.851816e-04 7.473835e-01 1.851816e-04 7.473835e-01 8.693217e-01 1.851816e-04
## 14781 14787 14817 14819 14860 14895
## 4.817649e-04 7.473835e-01 1.283902e-03 1.851816e-04 5.317688e-05 7.865246e-06
## 14907 14925 14945 14947 14955 14963
## 7.473835e-01 1.851816e-04 7.473835e-01 3.124682e-02 1.361263e-05 2.139593e-05
## 15005 15024 15037 15096 15134 15143
## 2.674842e-01 1.851816e-04 1.851816e-04 7.473835e-01 7.865246e-06 1.415560e-01
## 15194 15196 15200 15217 15220 15242
## 8.309411e-02 3.671491e-05 7.865246e-06 6.987382e-05 1.361263e-05 3.342656e-05
## 15249
## 7.473835e-01hist(eigenvector_cetrality, labels = TRUE, ylim = c(0,1000), xlab = "Eigenvector Centrality", main = "Histograma de los Valores de Eigenvector Centrality")
Según este histograma, hay nodos que tienen un eigenvector centrality mayor a 0.8. Veamos Cuáles son:
which(eigenvector_cetrality >=0.8)## 134 679 1175 1467 1833 2316 2329 2426 2427 2660 2846 3009 3200
## 15 56 90 110 130 157 158 169 170 194 206 217 236
## 3265 3516 4010 4730 5049 5714 5846 5920 5996 6630 6815 6869 8123
## 239 259 289 331 360 404 409 423 430 472 482 486 576
## 8992 9098 9659 10974 11234 11707 11735 12147 12216 12335 12707 13000 14404
## 630 638 672 761 778 806 809 835 839 843 879 903 997
## 14762
## 1019colorfunc <- colorRampPalette(c( "Medium Slate Blue",
"Magenta",
"Maroon 2",
"Purple",
"red",
"firebrick",
"lightcoral",
"saddlebrown",
"tomato",
"darkorange",
"gold",
"lightgreen",
"mediumspringgreen",
"dodgerblue",
"steelblue",
"Light Steel Blue 1",
"Royal Blue 2"))
scaled_eigen = (eigenvector_cetrality * 100) + 1
colors <- colorfunc(max(scaled_eigen))
V(users_projected)$color <- colors[scaled_eigen]
V(users_projected)$shape <- "circle"
E(users_projected)$color <- "lightgray"
plot(users_projected,
vertex.label = format(round(eigenvector_cetrality, 2), nsmall = 1),
vertex.size = 3,
edge.arrow.size=0.1,
layout=layout_components,
main = "Proyección Red Noticias: Nodos según el Eigenvector Centrality")
plot(users_projected,
vertex.label = NA,
vertex.size = 3,
edge.arrow.size=0.1,
layout=layout_components,
main = "Proyección Red Usuarios: Nodos según el Eigenvector Centrality")
Page Rank Centrality
page_rank = page.rank(users_projected)$vector
page_rank## 10 11 41 51 114 67
## 0.0011713097 0.0009388818 0.0003482703 0.0009341838 0.0002651061 0.0010698138
## 78 82 85 98 107 128
## 0.0007080711 0.0011368758 0.0002029969 0.0010698138 0.0011713097 0.0008358283
## 132 133 134 143 144 169
## 0.0004481219 0.0003217516 0.0013881179 0.0004175369 0.0003482703 0.0011713097
## 182 187 189 203 231 234
## 0.0004594431 0.0006438881 0.0010698138 0.0009341838 0.0009341838 0.0011713097
## 246 254 257 258 281 306
## 0.0007080711 0.0012193089 0.0009388818 0.0015886603 0.0010698138 0.0006438881
## 345 349 354 376 393 398
## 0.0009388818 0.0009388818 0.0010698138 0.0004873581 0.0005565868 0.0009388818
## 448 456 466 470 471 478
## 0.0010698138 0.0004481219 0.0016721165 0.0010698138 0.0004996982 0.0009341838
## 499 519 521 522 529 533
## 0.0009388818 0.0003227964 0.0009341838 0.0016737323 0.0009341838 0.0009388818
## 568 595 620 639 648 656
## 0.0015476963 0.0003742665 0.0010698138 0.0009388818 0.0004873581 0.0011713097
## 657 679 697 743 771 796
## 0.0006163682 0.0013881179 0.0007080711 0.0004175369 0.0016737323 0.0009388818
## 822 837 838 840 841 859
## 0.0009388818 0.0004175369 0.0006387061 0.0010698138 0.0004481219 0.0009341838
## 861 873 917 940 946 948
## 0.0009388818 0.0009388818 0.0010698138 0.0016141220 0.0009388818 0.0004996982
## 968 973 976 994 1000 1002
## 0.0008208890 0.0011713097 0.0006438881 0.0010698138 0.0010698138 0.0004594431
## 1024 1031 1032 1039 1055 1076
## 0.0011713097 0.0006387061 0.0008358283 0.0011713097 0.0004175369 0.0010698138
## 1102 1110 1138 1163 1172 1175
## 0.0011713097 0.0012785422 0.0011713097 0.0016762313 0.0011713097 0.0013881179
## 1209 1214 1218 1223 1224 1257
## 0.0009341838 0.0011713097 0.0010698138 0.0011713097 0.0010698138 0.0011713097
## 1258 1280 1307 1313 1329 1362
## 0.0006438881 0.0011713097 0.0007378925 0.0004594431 0.0011713097 0.0002651061
## 1412 1415 1416 1424 1429 1445
## 0.0010698138 0.0009341838 0.0010698138 0.0009341838 0.0003482703 0.0004873581
## 1462 1467 1481 1483 1494 1507
## 0.0003482703 0.0013881179 0.0009341838 0.0006387061 0.0004594431 0.0010698138
## 1544 1559 1596 1598 1639 1657
## 0.0016737323 0.0014386494 0.0010698138 0.0003482703 0.0007080711 0.0003217516
## 1660 1705 1709 1718 1739 1752
## 0.0010698138 0.0011713097 0.0009341838 0.0010848986 0.0007080711 0.0009341838
## 1760 1790 1826 1833 1849 1884
## 0.0007080711 0.0009388818 0.0002923049 0.0018620013 0.0010698138 0.0007080711
## 1898 1902 1924 1933 1984 1994
## 0.0016737323 0.0009341838 0.0009388818 0.0011713097 0.0011713097 0.0011713097
## 2001 2011 2019 2026 2042 2063
## 0.0009388818 0.0007080711 0.0006438881 0.0010698138 0.0021907777 0.0008358283
## 2084 2129 2143 2149 2188 2192
## 0.0010698138 0.0010698138 0.0010698138 0.0010698138 0.0011478567 0.0009388818
## 2193 2199 2227 2250 2286 2293
## 0.0010698138 0.0006387061 0.0015476963 0.0008358283 0.0011713097 0.0009341838
## 2316 2329 2330 2335 2349 2350
## 0.0013881179 0.0013881179 0.0009388818 0.0007080711 0.0004594431 0.0010698138
## 2377 2380 2388 2393 2405 2415
## 0.0004873581 0.0010698138 0.0011713097 0.0007080711 0.0004175369 0.0009341838
## 2426 2427 2460 2462 2466 2471
## 0.0021799494 0.0030622740 0.0006387061 0.0010698138 0.0004996982 0.0010698138
## 2484 2491 2492 2502 2504 2516
## 0.0011713097 0.0004594431 0.0011713097 0.0011713097 0.0009341838 0.0006438881
## 2524 2526 2527 2533 2542 2549
## 0.0007080711 0.0010593073 0.0010698138 0.0002894517 0.0010698138 0.0011713097
## 2557 2562 2582 2596 2601 2603
## 0.0008358283 0.0011478567 0.0004481219 0.0015348569 0.0011713097 0.0009341838
## 2623 2660 2663 2701 2720 2730
## 0.0005565868 0.0013881179 0.0012131444 0.0005565868 0.0006438881 0.0009388818
## 2738 2795 2806 2811 2820 2835
## 0.0009388818 0.0010698138 0.0002207343 0.0010698138 0.0002696208 0.0010698138
## 2844 2846 2849 2861 2872 2876
## 0.0010698138 0.0013881179 0.0010698138 0.0003217516 0.0002900729 0.0009341838
## 2908 2920 2951 2961 2986 2987
## 0.0011713097 0.0011713097 0.0004873581 0.0007080711 0.0009341838 0.0005565868
## 3009 3020 3029 3051 3058 3065
## 0.0013881179 0.0004594431 0.0010698138 0.0011713097 0.0009388818 0.0010698138
## 3066 3091 3103 3119 3134 3136
## 0.0011713097 0.0010698138 0.0005565868 0.0010698138 0.0004175369 0.0006438881
## 3137 3150 3152 3158 3167 3186
## 0.0004996982 0.0010698138 0.0017612924 0.0012513715 0.0005565868 0.0010698138
## 3187 3200 3230 3260 3265 3278
## 0.0014898330 0.0017529683 0.0011713097 0.0002580986 0.0015990236 0.0009388818
## 3298 3314 3327 3345 3350 3356
## 0.0010698138 0.0010698138 0.0010698138 0.0010698138 0.0011713097 0.0005118496
## 3384 3397 3401 3404 3408 3411
## 0.0010698138 0.0012075695 0.0006438881 0.0007080711 0.0010698138 0.0007080711
## 3443 3459 3461 3469 3491 3505
## 0.0010698138 0.0010698138 0.0003742665 0.0009341838 0.0006387061 0.0009388818
## 3516 3519 3533 3549 3554 3557
## 0.0022102613 0.0015476963 0.0011713097 0.0006438881 0.0009341838 0.0010698138
## 3571 3577 3595 3596 3635 3648
## 0.0011713097 0.0007080711 0.0007080711 0.0009341838 0.0004594431 0.0010698138
## 3651 3723 3727 3728 3733 3750
## 0.0009341838 0.0011713097 0.0011233504 0.0009341838 0.0005118496 0.0011713097
## 3752 3753 3790 3833 3845 3857
## 0.0009341838 0.0004594431 0.0011713097 0.0010698138 0.0003227964 0.0009388818
## 3873 3893 3940 3965 3987 3989
## 0.0011309105 0.0011713097 0.0007080711 0.0004873581 0.0006387061 0.0009388818
## 4010 4011 4014 4068 4070 4080
## 0.0013881179 0.0005565868 0.0010698138 0.0008208890 0.0010698138 0.0003742665
## 4081 4102 4124 4159 4214 4229
## 0.0010698138 0.0011713097 0.0008358283 0.0011713097 0.0004873581 0.0010698138
## 4243 4245 4273 4276 4303 4304
## 0.0010698138 0.0004873581 0.0010698138 0.0009388818 0.0011713097 0.0009341838
## 4310 4323 4350 4352 4369 4374
## 0.0011713097 0.0010698138 0.0002580986 0.0002894517 0.0010698138 0.0005118496
## 4384 4432 4461 4470 4472 4476
## 0.0017390477 0.0010698138 0.0008358283 0.0009388818 0.0011713097 0.0011713097
## 4489 4526 4536 4542 4550 4590
## 0.0015886603 0.0010698138 0.0011713097 0.0009388818 0.0004481219 0.0004435801
## 4600 4615 4633 4667 4705 4708
## 0.0011713097 0.0004873581 0.0010698138 0.0010698138 0.0005565868 0.0011713097
## 4730 4750 4751 4756 4760 4792
## 0.0013881179 0.0011713097 0.0005118496 0.0005118496 0.0010698138 0.0009341838
## 4800 4801 4820 4822 4823 4833
## 0.0002723135 0.0006438881 0.0009341838 0.0011713097 0.0004481219 0.0011713097
## 4843 4846 4862 4864 4875 4879
## 0.0012785422 0.0011713097 0.0005565868 0.0010698138 0.0005118496 0.0008358283
## 4890 4904 4910 4921 4930 4954
## 0.0009388818 0.0006387061 0.0010698138 0.0009341838 0.0006272817 0.0006387061
## 4964 4982 5014 5015 5044 5049
## 0.0005118496 0.0006387061 0.0003482703 0.0011713097 0.0005565868 0.0013881179
## 5055 5057 5072 5103 5115 5120
## 0.0010593073 0.0010698138 0.0010698138 0.0008358283 0.0016737323 0.0017314597
## 5132 5136 5146 5179 5189 5223
## 0.0003742665 0.0008791931 0.0007080711 0.0011713097 0.0007080711 0.0010698138
## 5231 5251 5292 5293 5327 5331
## 0.0011713097 0.0004873581 0.0004873581 0.0011713097 0.0011713097 0.0009388818
## 5342 5359 5423 5424 5438 5446
## 0.0010698138 0.0006387061 0.0010698138 0.0006387061 0.0010698138 0.0007080711
## 5453 5461 5466 5507 5508 5531
## 0.0009341838 0.0002723135 0.0020169136 0.0010593073 0.0010354461 0.0010698138
## 5547 5552 5554 5556 5564 5579
## 0.0011713097 0.0016721165 0.0009388818 0.0010698138 0.0006438881 0.0002599620
## 5583 5597 5624 5666 5670 5688
## 0.0010698138 0.0005118496 0.0011713097 0.0011713097 0.0011713097 0.0011713097
## 5707 5714 5726 5731 5771 5793
## 0.0011713097 0.0013881179 0.0010698138 0.0009388818 0.0010698138 0.0011713097
## 5846 5849 5867 5881 5886 5899
## 0.0013881179 0.0010698138 0.0011713097 0.0011713097 0.0004996982 0.0007080711
## 5901 5902 5904 5905 5906 5911
## 0.0011713097 0.0011713097 0.0011713097 0.0004996982 0.0010698138 0.0009341838
## 5912 5918 5920 5939 5948 5962
## 0.0009388818 0.0011713097 0.0013881179 0.0005565868 0.0007080711 0.0015886603
## 5965 5973 5994 5996 5999 6003
## 0.0010698138 0.0010698138 0.0011713097 0.0030869325 0.0005565868 0.0011713097
## 6009 6018 6022 6054 6078 6085
## 0.0012785422 0.0017390477 0.0006387061 0.0023188414 0.0010105412 0.0011713097
## 6118 6121 6125 6134 6145 6163
## 0.0010698138 0.0010698138 0.0009341838 0.0006438881 0.0005118496 0.0010698138
## 6169 6173 6190 6202 6221 6223
## 0.0006438881 0.0003227964 0.0011713097 0.0003742665 0.0009388818 0.0010698138
## 6258 6322 6336 6345 6349 6350
## 0.0010698138 0.0009388818 0.0007080711 0.0003500186 0.0006387061 0.0006438881
## 6354 6367 6370 6373 6391 6404
## 0.0009388818 0.0011713097 0.0011713097 0.0004996982 0.0011713097 0.0009388818
## 6451 6472 6498 6505 6554 6563
## 0.0010698138 0.0011713097 0.0011713097 0.0010698138 0.0010698138 0.0009388818
## 6576 6591 6600 6630 6647 6679
## 0.0009341838 0.0015886603 0.0004873581 0.0018620013 0.0003500186 0.0011713097
## 6690 6695 6742 6757 6764 6784
## 0.0006438881 0.0011713097 0.0010698138 0.0010698138 0.0009388818 0.0015886603
## 6785 6815 6821 6857 6860 6869
## 0.0011713097 0.0023655262 0.0010698138 0.0006387061 0.0010698138 0.0013881179
## 6876 6880 6901 6914 6918 6920
## 0.0003500186 0.0016762313 0.0009388818 0.0002153317 0.0002508441 0.0010698138
## 6938 6939 6943 6946 6949 6955
## 0.0011713097 0.0002651061 0.0011713097 0.0011713097 0.0010698138 0.0009341838
## 6962 7026 7041 7048 7082 7103
## 0.0015886603 0.0010698138 0.0009388818 0.0010698138 0.0010698138 0.0010698138
## 7109 7116 7129 7137 7194 7202
## 0.0006438881 0.0005565868 0.0010698138 0.0007080711 0.0011713097 0.0010698138
## 7242 7261 7277 7295 7313 7316
## 0.0010698138 0.0011713097 0.0003500186 0.0004996982 0.0009388818 0.0011713097
## 7335 7337 7345 7352 7367 7373
## 0.0011713097 0.0004175369 0.0005565868 0.0011713097 0.0010698138 0.0016737323
## 7375 7392 7397 7398 7401 7430
## 0.0010698138 0.0010698138 0.0005565868 0.0011713097 0.0009388818 0.0004873581
## 7456 7458 7461 7472 7480 7482
## 0.0009388818 0.0006387061 0.0009341838 0.0008358283 0.0010698138 0.0011713097
## 7483 7508 7514 7564 7566 7569
## 0.0019230111 0.0011713097 0.0010698138 0.0003482703 0.0009341838 0.0012030072
## 7578 7619 7623 7624 7649 7697
## 0.0011713097 0.0010698138 0.0010698138 0.0017390477 0.0007080711 0.0009388818
## 7718 7731 7746 7756 7762 7765
## 0.0011713097 0.0011713097 0.0006387061 0.0016737323 0.0002923049 0.0009341838
## 7772 7779 7825 7830 7837 7850
## 0.0014647940 0.0010698138 0.0005118496 0.0005565868 0.0009341838 0.0007080711
## 7859 7862 7887 7890 7902 7904
## 0.0006387061 0.0005118496 0.0011713097 0.0007080711 0.0004594431 0.0010698138
## 7905 7941 7962 7964 7966 7983
## 0.0011713097 0.0011713097 0.0004873581 0.0009388818 0.0009341838 0.0011713097
## 8006 8015 8030 8077 8113 8123
## 0.0009388818 0.0011713097 0.0005118496 0.0011713097 0.0011713097 0.0013881179
## 8133 8134 8168 8174 8181 8183
## 0.0009341838 0.0017814425 0.0006387061 0.0004594431 0.0006438881 0.0011713097
## 8208 8220 8223 8253 8267 8293
## 0.0011713097 0.0010698138 0.0010698138 0.0006387061 0.0012047979 0.0011713097
## 8294 8309 8338 8342 8362 8365
## 0.0010698138 0.0012560040 0.0009341838 0.0011713097 0.0005565868 0.0006438881
## 8370 8373 8382 8393 8413 8426
## 0.0010105412 0.0009341838 0.0011713097 0.0004996982 0.0009388818 0.0009341838
## 8485 8542 8546 8566 8593 8609
## 0.0010698138 0.0011713097 0.0010698138 0.0009341838 0.0010698138 0.0016737323
## 8627 8663 8679 8708 8742 8746
## 0.0003742665 0.0004873581 0.0006438881 0.0005118496 0.0009388818 0.0004594431
## 8751 8761 8783 8787 8798 8825
## 0.0016721165 0.0011713097 0.0004996982 0.0004996982 0.0011713097 0.0009341838
## 8831 8850 8868 8878 8880 8894
## 0.0011713097 0.0009388818 0.0010698138 0.0011233504 0.0012485819 0.0009341838
## 8914 8931 8939 8953 8957 8992
## 0.0006387061 0.0011713097 0.0006387061 0.0009388818 0.0004996982 0.0013881179
## 8997 9015 9018 9025 9048 9061
## 0.0009341838 0.0010698138 0.0002696208 0.0010698138 0.0004594431 0.0004996982
## 9095 9098 9117 9129 9140 9163
## 0.0011713097 0.0018620013 0.0011713097 0.0026597561 0.0011295432 0.0005565868
## 9204 9210 9228 9229 9250 9253
## 0.0010698138 0.0010698138 0.0011713097 0.0011713097 0.0012485819 0.0009341838
## 9309 9310 9320 9341 9350 9355
## 0.0004873581 0.0010698138 0.0003227964 0.0010698138 0.0009341838 0.0010698138
## 9358 9373 9384 9407 9413 9424
## 0.0007980112 0.0009388818 0.0004873581 0.0007080711 0.0006387061 0.0011713097
## 9481 9486 9500 9553 9568 9570
## 0.0004996982 0.0009388818 0.0009388818 0.0004594431 0.0011713097 0.0007080711
## 9596 9607 9619 9641 9652 9659
## 0.0006438881 0.0002894517 0.0011713097 0.0007080711 0.0004594431 0.0015990236
## 9661 9664 9679 9682 9684 9714
## 0.0010698138 0.0005565868 0.0010105412 0.0010698138 0.0011713097 0.0008358283
## 9726 9763 9766 9782 9785 9792
## 0.0022391695 0.0022391695 0.0006438881 0.0002820643 0.0010698138 0.0007080711
## 9794 9809 9816 9834 9848 9851
## 0.0010698138 0.0006438881 0.0011713097 0.0011713097 0.0017814425 0.0005565868
## 9857 9877 9895 9905 9920 10003
## 0.0001589419 0.0010698138 0.0002820643 0.0011713097 0.0006438881 0.0011713097
## 10012 10054 10105 10110 10117 10118
## 0.0011713097 0.0015003231 0.0014189756 0.0005118496 0.0009388818 0.0002820643
## 10130 10155 10178 10201 10206 10209
## 0.0010698138 0.0010698138 0.0012785422 0.0010698138 0.0019175970 0.0010698138
## 10212 10241 10262 10270 10279 10290
## 0.0004481219 0.0026597561 0.0006438881 0.0010698138 0.0006387061 0.0011713097
## 10303 10364 10371 10386 10404 10423
## 0.0011713097 0.0015886603 0.0010698138 0.0019670350 0.0012173047 0.0007080711
## 10439 10449 10475 10480 10487 10500
## 0.0011713097 0.0009341838 0.0001752357 0.0007080711 0.0011713097 0.0010698138
## 10512 10516 10527 10544 10549 10568
## 0.0004996982 0.0004873581 0.0010698138 0.0009341838 0.0011713097 0.0009341838
## 10572 10582 10590 10638 10650 10694
## 0.0011713097 0.0007080711 0.0009341838 0.0018500530 0.0012809606 0.0004873581
## 10708 10724 10730 10736 10759 10771
## 0.0004175369 0.0006387061 0.0010698138 0.0011713097 0.0010698138 0.0004996982
## 10774 10776 10782 10802 10808 10836
## 0.0010698138 0.0005565868 0.0009388818 0.0010698138 0.0011713097 0.0003217516
## 10862 10874 10882 10886 10891 10906
## 0.0010698138 0.0004594431 0.0009341838 0.0008358283 0.0011233504 0.0010698138
## 10920 10929 10944 10952 10974 10995
## 0.0009341838 0.0010698138 0.0010698138 0.0006438881 0.0013881179 0.0011713097
## 11011 11025 11044 11073 11074 11123
## 0.0006438881 0.0011713097 0.0004996982 0.0002723135 0.0009341838 0.0005565868
## 11132 11142 11146 11156 11164 11171
## 0.0009388818 0.0005565868 0.0010698138 0.0003482703 0.0002900729 0.0003482703
## 11178 11217 11225 11234 11257 11296
## 0.0007080711 0.0011713097 0.0010698138 0.0029310713 0.0010698138 0.0011713097
## 11302 11305 11342 11352 11388 11390
## 0.0004175369 0.0007080711 0.0016260007 0.0006387061 0.0005118496 0.0010698138
## 11396 11417 11443 11456 11457 11461
## 0.0008358283 0.0011713097 0.0004873581 0.0003500186 0.0004481219 0.0002508441
## 11504 11527 11559 11560 11592 11606
## 0.0006438881 0.0009388818 0.0009341838 0.0011713097 0.0004594431 0.0010698138
## 11608 11642 11664 11671 11678 11700
## 0.0010901086 0.0010698138 0.0009388818 0.0011713097 0.0007080711 0.0010698138
## 11704 11707 11728 11729 11735 11753
## 0.0010698138 0.0018620013 0.0010593073 0.0003227964 0.0013881179 0.0005565868
## 11757 11818 11820 11839 11843 11859
## 0.0010698138 0.0009388818 0.0007080711 0.0009388818 0.0005118496 0.0009341838
## 11862 11887 11896 11935 11980 11988
## 0.0009388818 0.0017672715 0.0011713097 0.0010698138 0.0004175369 0.0010698138
## 12016 12030 12032 12049 12051 12075
## 0.0009341838 0.0010698138 0.0011713097 0.0009341838 0.0005118496 0.0011713097
## 12084 12091 12109 12114 12117 12128
## 0.0011713097 0.0010698138 0.0023392465 0.0009388818 0.0006387061 0.0008358283
## 12147 12160 12166 12190 12216 12236
## 0.0013881179 0.0009388818 0.0003217516 0.0011713097 0.0013881179 0.0009341838
## 12283 12307 12335 12367 12372 12376
## 0.0004594431 0.0005118496 0.0013881179 0.0011713097 0.0008358283 0.0011713097
## 12380 12382 12389 12395 12397 12408
## 0.0009341838 0.0006387061 0.0004594431 0.0004594431 0.0009341838 0.0010698138
## 12427 12439 12450 12452 12465 12468
## 0.0011713097 0.0003500186 0.0009388818 0.0009388818 0.0010698138 0.0007080711
## 12490 12496 12522 12527 12528 12537
## 0.0009341838 0.0009341838 0.0006438881 0.0005565868 0.0010698138 0.0010698138
## 12546 12550 12552 12554 12561 12568
## 0.0006438881 0.0011713097 0.0011713097 0.0004175369 0.0012173047 0.0011713097
## 12570 12579 12593 12607 12609 12622
## 0.0010698138 0.0010698138 0.0004873581 0.0010698138 0.0011713097 0.0006387061
## 12648 12705 12707 12739 12751 12761
## 0.0011713097 0.0009388818 0.0013881179 0.0003227964 0.0009388818 0.0004608206
## 12782 12799 12804 12806 12816 12843
## 0.0009388818 0.0010698138 0.0006438881 0.0010698138 0.0003742665 0.0006387061
## 12887 12888 12894 12901 12906 12918
## 0.0010698138 0.0010698138 0.0011713097 0.0016762313 0.0010698138 0.0006387061
## 12933 12943 12948 12956 12964 12968
## 0.0008741995 0.0005565868 0.0022391695 0.0012785422 0.0009388818 0.0010698138
## 12980 12984 13000 13035 13052 13087
## 0.0011713097 0.0010698138 0.0013881179 0.0010698138 0.0009388818 0.0016737323
## 13092 13104 13111 13133 13136 13137
## 0.0011713097 0.0011713097 0.0009341838 0.0011713097 0.0014647940 0.0009388818
## 13138 13143 13150 13154 13159 13188
## 0.0004175369 0.0010698138 0.0004996982 0.0007080711 0.0011713097 0.0010698138
## 13252 13261 13263 13284 13335 13336
## 0.0010698138 0.0011713097 0.0002723135 0.0007080711 0.0007080711 0.0010698138
## 13341 13360 13396 13450 13469 13497
## 0.0011713097 0.0009341838 0.0010698138 0.0010698138 0.0003742665 0.0007080711
## 13540 13553 13565 13586 13602 13621
## 0.0014189756 0.0011713097 0.0004175369 0.0006438881 0.0009341838 0.0007080711
## 13635 13636 13647 13656 13660 13674
## 0.0004481219 0.0004996982 0.0010698138 0.0007080711 0.0006387061 0.0010698138
## 13675 13682 13690 13732 13737 13742
## 0.0010698138 0.0009388818 0.0007080711 0.0010698138 0.0005118496 0.0009341838
## 13753 13755 13761 13777 13778 13782
## 0.0010698138 0.0011713097 0.0011713097 0.0023200404 0.0004175369 0.0009341838
## 13784 13791 13807 13892 13916 13962
## 0.0011713097 0.0009388818 0.0010698138 0.0010698138 0.0002153317 0.0010698138
## 13967 13989 14009 14035 14037 14060
## 0.0011713097 0.0011713097 0.0005565868 0.0010698138 0.0011713097 0.0011713097
## 14061 14064 14085 14095 14104 14110
## 0.0004594431 0.0003500186 0.0010698138 0.0009341838 0.0008358283 0.0006438881
## 14118 14119 14122 14125 14135 14137
## 0.0007080711 0.0015476963 0.0010698138 0.0010698138 0.0010698138 0.0011713097
## 14158 14187 14203 14204 14210 14216
## 0.0006438881 0.0011713097 0.0009341838 0.0007807693 0.0010698138 0.0005118496
## 14221 14256 14305 14311 14318 14321
## 0.0006438881 0.0003217516 0.0010698138 0.0005384504 0.0010698138 0.0011713097
## 14346 14358 14362 14368 14374 14403
## 0.0021005459 0.0009341838 0.0011713097 0.0011713097 0.0009388818 0.0011713097
## 14404 14414 14422 14435 14443 14481
## 0.0041089722 0.0011713097 0.0010698138 0.0010698138 0.0006387061 0.0009388818
## 14543 14571 14581 14592 14609 14628
## 0.0010698138 0.0002820643 0.0004594431 0.0010698138 0.0002599620 0.0010698138
## 14633 14635 14664 14683 14692 14725
## 0.0006387061 0.0009341838 0.0010698138 0.0008358283 0.0009341838 0.0011713097
## 14741 14743 14747 14751 14762 14780
## 0.0011713097 0.0010698138 0.0011713097 0.0010698138 0.0013881179 0.0011713097
## 14781 14787 14817 14819 14860 14895
## 0.0004691925 0.0010698138 0.0009388818 0.0011713097 0.0008358283 0.0006438881
## 14907 14925 14945 14947 14955 14963
## 0.0010698138 0.0011713097 0.0010698138 0.0004873581 0.0007080711 0.0012106318
## 15005 15024 15037 15096 15134 15143
## 0.0018075839 0.0011713097 0.0011713097 0.0010698138 0.0006438881 0.0014647940
## 15194 15196 15200 15217 15220 15242
## 0.0009341838 0.0006387061 0.0006438881 0.0010105412 0.0007080711 0.0005118496
## 15249
## 0.0010698138hist(page_rank, labels = TRUE, ylim = c(0,500), xlim = c(0,0.005), xlab = "PageRank Centrality", main = "Histograma de los Valores de PageRank Centrality")
colorfunc <- colorRampPalette(c( "Medium Slate Blue",
"Magenta",
"Maroon 2",
"Purple",
"red",
"firebrick",
"lightcoral",
"saddlebrown",
"tomato",
"darkorange",
"gold",
"lightgreen",
"mediumspringgreen",
"dodgerblue",
"steelblue",
"Light Steel Blue 1",
"Royal Blue 2"))
scaled_page_rank = (page_rank * 1000) + 1
colors <- colorfunc(max(scaled_page_rank))
V(users_projected)$color <- colors[scaled_page_rank]
V(users_projected)$shape <- "circle"
E(users_projected)$color <- "lightgray"
plot(users_projected,
vertex.label = format(round(scaled_page_rank), nsmall = 1),
vertex.size = 2,
edge.arrow.size=0.1,
layout=layout_components,
main = "Proyección Red Usuarios: Nodos según Page Rank Score Escalado")
plot(users_projected,
vertex.label = NA,
vertex.size = 3,
edge.arrow.size=0.1,
layout=layout_components,
main = "Proyección Red Usuarios: Nodos según Page Rank Score Escalado")
Authorities & Hubs
Authorities
authority = authority.score(users_projected)$vector
authority## 10 11 41 51 114 67
## 1.851816e-04 1.283902e-03 3.094893e-02 8.309411e-02 8.051461e-03 7.473835e-01
## 78 82 85 98 107 128
## 1.361263e-05 1.320908e-03 3.240013e-04 7.473835e-01 1.851816e-04 5.317688e-05
## 132 133 134 143 144 169
## 1.632376e-06 8.568722e-03 8.693217e-01 1.384216e-02 3.094893e-02 1.851816e-04
## 182 187 189 203 231 234
## 1.252681e-01 7.865246e-06 7.473835e-01 8.309411e-02 8.309411e-02 1.851816e-04
## 246 254 257 258 281 306
## 1.361263e-05 1.506834e-02 1.283902e-03 1.955626e-04 7.473835e-01 7.865246e-06
## 345 349 354 376 393 398
## 1.283902e-03 1.283902e-03 7.473835e-01 3.124682e-02 1.113006e-05 1.283902e-03
## 448 456 466 470 471 478
## 7.473835e-01 1.632376e-06 2.210498e-04 7.473835e-01 1.292210e-02 8.309411e-02
## 499 519 521 522 529 533
## 1.283902e-03 2.829945e-02 8.309411e-02 1.716172e-01 8.309411e-02 1.283902e-03
## 568 595 620 639 648 656
## 2.177740e-04 3.192423e-06 7.473835e-01 1.283902e-03 3.124682e-02 1.851816e-04
## 657 679 697 743 771 796
## 3.914832e-02 8.693217e-01 1.361263e-05 1.384216e-02 1.716172e-01 1.283902e-03
## 822 837 838 840 841 859
## 1.283902e-03 1.384216e-02 3.671491e-05 7.473835e-01 1.632376e-06 8.309411e-02
## 861 873 917 940 946 948
## 1.283902e-03 1.283902e-03 7.473835e-01 2.196020e-01 1.283902e-03 1.292210e-02
## 968 973 976 994 1000 1002
## 1.376629e-01 1.851816e-04 7.865246e-06 7.473835e-01 7.473835e-01 1.252681e-01
## 1024 1031 1032 1039 1055 1076
## 1.851816e-04 3.671491e-05 5.317688e-05 1.851816e-04 1.384216e-02 7.473835e-01
## 1102 1110 1138 1163 1172 1175
## 1.851816e-04 9.947152e-02 1.851816e-04 1.923103e-04 1.851816e-04 8.693217e-01
## 1209 1214 1218 1223 1224 1257
## 8.309411e-02 1.851816e-04 7.473835e-01 1.851816e-04 7.473835e-01 1.851816e-04
## 1258 1280 1307 1313 1329 1362
## 7.865246e-06 1.851816e-04 1.147608e-05 1.252681e-01 1.851816e-04 8.051461e-03
## 1412 1415 1416 1424 1429 1445
## 7.473835e-01 8.309411e-02 7.473835e-01 8.309411e-02 3.094893e-02 3.124682e-02
## 1462 1467 1481 1483 1494 1507
## 3.094893e-02 8.693217e-01 8.309411e-02 3.671491e-05 1.252681e-01 7.473835e-01
## 1544 1559 1596 1598 1639 1657
## 1.716172e-01 1.016310e-01 7.473835e-01 3.094893e-02 1.361263e-05 8.568722e-03
## 1660 1705 1709 1718 1739 1752
## 7.473835e-01 1.851816e-04 8.309411e-02 5.977311e-06 1.361263e-05 8.309411e-02
## 1760 1790 1826 1833 1849 1884
## 1.361263e-05 1.283902e-03 4.206519e-05 8.273087e-01 7.473835e-01 1.361263e-05
## 1898 1902 1924 1933 1984 1994
## 1.716172e-01 8.309411e-02 1.283902e-03 1.851816e-04 1.851816e-04 1.851816e-04
## 2001 2011 2019 2026 2042 2063
## 1.283902e-03 1.361263e-05 7.865246e-06 7.473835e-01 6.573193e-03 5.317688e-05
## 2084 2129 2143 2149 2188 2192
## 7.473835e-01 7.473835e-01 7.473835e-01 7.473835e-01 9.183034e-02 1.283902e-03
## 2193 2199 2227 2250 2286 2293
## 7.473835e-01 3.671491e-05 2.177740e-04 5.317688e-05 1.851816e-04 8.309411e-02
## 2316 2329 2330 2335 2349 2350
## 8.693217e-01 8.693217e-01 1.283902e-03 1.361263e-05 1.252681e-01 7.473835e-01
## 2377 2380 2388 2393 2405 2415
## 3.124682e-02 7.473835e-01 1.851816e-04 1.361263e-05 1.384216e-02 8.309411e-02
## 2426 2427 2460 2462 2466 2471
## 9.484796e-01 9.216813e-01 3.671491e-05 7.473835e-01 1.292210e-02 7.473835e-01
## 2484 2491 2492 2502 2504 2516
## 1.851816e-04 1.252681e-01 1.851816e-04 1.851816e-04 8.309411e-02 7.865246e-06
## 2524 2526 2527 2533 2542 2549
## 1.361263e-05 1.892283e-05 7.473835e-01 3.899759e-07 7.473835e-01 1.851816e-04
## 2557 2562 2582 2596 2601 2603
## 5.317688e-05 9.183034e-02 1.632376e-06 2.205150e-01 1.851816e-04 8.309411e-02
## 2623 2660 2663 2701 2720 2730
## 1.113006e-05 8.693217e-01 1.951956e-01 1.113006e-05 7.865246e-06 1.283902e-03
## 2738 2795 2806 2811 2820 2835
## 1.283902e-03 7.473835e-01 6.423139e-04 7.473835e-01 1.534919e-06 7.473835e-01
## 2844 2846 2849 2861 2872 2876
## 7.473835e-01 8.693217e-01 7.473835e-01 8.568722e-03 4.415451e-04 8.309411e-02
## 2908 2920 2951 2961 2986 2987
## 1.851816e-04 1.851816e-04 3.124682e-02 1.361263e-05 8.309411e-02 1.113006e-05
## 3009 3020 3029 3051 3058 3065
## 8.693217e-01 1.252681e-01 7.473835e-01 1.851816e-04 1.283902e-03 7.473835e-01
## 3066 3091 3103 3119 3134 3136
## 1.851816e-04 7.473835e-01 1.113006e-05 7.473835e-01 1.384216e-02 7.865246e-06
## 3137 3150 3152 3158 3167 3186
## 1.292210e-02 7.473835e-01 8.318815e-05 2.075671e-01 1.113006e-05 7.473835e-01
## 3187 3200 3230 3260 3265 3278
## 1.441853e-01 8.788411e-01 1.851816e-04 3.859943e-03 8.967309e-01 1.283902e-03
## 3298 3314 3327 3345 3350 3356
## 7.473835e-01 7.473835e-01 7.473835e-01 7.473835e-01 1.851816e-04 3.342656e-05
## 3384 3397 3401 3404 3408 3411
## 7.473835e-01 9.679743e-02 7.865246e-06 1.361263e-05 7.473835e-01 1.361263e-05
## 3443 3459 3461 3469 3491 3505
## 7.473835e-01 7.473835e-01 3.192423e-06 8.309411e-02 3.671491e-05 1.283902e-03
## 3516 3519 3533 3549 3554 3557
## 8.551731e-01 2.177740e-04 1.851816e-04 7.865246e-06 8.309411e-02 7.473835e-01
## 3571 3577 3595 3596 3635 3648
## 1.851816e-04 1.361263e-05 1.361263e-05 8.309411e-02 1.252681e-01 7.473835e-01
## 3651 3723 3727 3728 3733 3750
## 8.309411e-02 1.851816e-04 2.464828e-05 8.309411e-02 3.342656e-05 1.851816e-04
## 3752 3753 3790 3833 3845 3857
## 8.309411e-02 1.252681e-01 1.851816e-04 7.473835e-01 2.829945e-02 1.283902e-03
## 3873 3893 3940 3965 3987 3989
## 9.815029e-03 1.851816e-04 1.361263e-05 3.124682e-02 3.671491e-05 1.283902e-03
## 4010 4011 4014 4068 4070 4080
## 8.693217e-01 1.113006e-05 7.473835e-01 1.376629e-01 7.473835e-01 3.192423e-06
## 4081 4102 4124 4159 4214 4229
## 7.473835e-01 1.851816e-04 5.317688e-05 1.851816e-04 3.124682e-02 7.473835e-01
## 4243 4245 4273 4276 4303 4304
## 7.473835e-01 3.124682e-02 7.473835e-01 1.283902e-03 1.851816e-04 8.309411e-02
## 4310 4323 4350 4352 4369 4374
## 1.851816e-04 7.473835e-01 3.859943e-03 3.899759e-07 7.473835e-01 3.342656e-05
## 4384 4432 4461 4470 4472 4476
## 1.980357e-04 7.473835e-01 5.317688e-05 1.283902e-03 1.851816e-04 1.851816e-04
## 4489 4526 4536 4542 4550 4590
## 1.955626e-04 7.473835e-01 1.851816e-04 1.283902e-03 1.632376e-06 1.012641e-02
## 4600 4615 4633 4667 4705 4708
## 1.851816e-04 3.124682e-02 7.473835e-01 7.473835e-01 1.113006e-05 1.851816e-04
## 4730 4750 4751 4756 4760 4792
## 8.693217e-01 1.851816e-04 3.342656e-05 3.342656e-05 7.473835e-01 8.309411e-02
## 4800 4801 4820 4822 4823 4833
## 6.393403e-03 7.865246e-06 8.309411e-02 1.851816e-04 1.632376e-06 1.851816e-04
## 4843 4846 4862 4864 4875 4879
## 9.947152e-02 1.851816e-04 1.113006e-05 7.473835e-01 3.342656e-05 5.317688e-05
## 4890 4904 4910 4921 4930 4954
## 1.283902e-03 3.671491e-05 7.473835e-01 8.309411e-02 2.089353e-02 3.671491e-05
## 4964 4982 5014 5015 5044 5049
## 3.342656e-05 3.671491e-05 3.094893e-02 1.851816e-04 1.113006e-05 8.693217e-01
## 5055 5057 5072 5103 5115 5120
## 1.892283e-05 7.473835e-01 7.473835e-01 5.317688e-05 1.716172e-01 8.405604e-02
## 5132 5136 5146 5179 5189 5223
## 3.192423e-06 8.980721e-02 1.361263e-05 1.851816e-04 1.361263e-05 7.473835e-01
## 5231 5251 5292 5293 5327 5331
## 1.851816e-04 3.124682e-02 3.124682e-02 1.851816e-04 1.851816e-04 1.283902e-03
## 5342 5359 5423 5424 5438 5446
## 7.473835e-01 3.671491e-05 7.473835e-01 3.671491e-05 7.473835e-01 1.361263e-05
## 5453 5461 5466 5507 5508 5531
## 8.309411e-02 6.393403e-03 1.964649e-04 1.892283e-05 8.385015e-02 7.473835e-01
## 5547 5552 5554 5556 5564 5579
## 1.851816e-04 2.210498e-04 1.283902e-03 7.473835e-01 7.865246e-06 8.406202e-07
## 5583 5597 5624 5666 5670 5688
## 7.473835e-01 3.342656e-05 1.851816e-04 1.851816e-04 1.851816e-04 1.851816e-04
## 5707 5714 5726 5731 5771 5793
## 1.851816e-04 8.693217e-01 7.473835e-01 1.283902e-03 7.473835e-01 1.851816e-04
## 5846 5849 5867 5881 5886 5899
## 8.693217e-01 7.473835e-01 1.851816e-04 1.851816e-04 1.292210e-02 1.361263e-05
## 5901 5902 5904 5905 5906 5911
## 1.851816e-04 1.851816e-04 1.851816e-04 1.292210e-02 7.473835e-01 8.309411e-02
## 5912 5918 5920 5939 5948 5962
## 1.283902e-03 1.851816e-04 8.693217e-01 1.113006e-05 1.361263e-05 1.955626e-04
## 5965 5973 5994 5996 5999 6003
## 7.473835e-01 7.473835e-01 1.851816e-04 9.221897e-01 1.113006e-05 1.851816e-04
## 6009 6018 6022 6054 6078 6085
## 9.947152e-02 1.980357e-04 3.671491e-05 1.994648e-01 6.987382e-05 1.851816e-04
## 6118 6121 6125 6134 6145 6163
## 7.473835e-01 7.473835e-01 8.309411e-02 7.865246e-06 3.342656e-05 7.473835e-01
## 6169 6173 6190 6202 6221 6223
## 7.865246e-06 2.829945e-02 1.851816e-04 3.192423e-06 1.283902e-03 7.473835e-01
## 6258 6322 6336 6345 6349 6350
## 7.473835e-01 1.283902e-03 1.361263e-05 9.087979e-03 3.671491e-05 7.865246e-06
## 6354 6367 6370 6373 6391 6404
## 1.283902e-03 1.851816e-04 1.851816e-04 1.292210e-02 1.851816e-04 1.283902e-03
## 6451 6472 6498 6505 6554 6563
## 7.473835e-01 1.851816e-04 1.851816e-04 7.473835e-01 7.473835e-01 1.283902e-03
## 6576 6591 6600 6630 6647 6679
## 8.309411e-02 1.955626e-04 3.124682e-02 8.273087e-01 9.087979e-03 1.851816e-04
## 6690 6695 6742 6757 6764 6784
## 7.865246e-06 1.851816e-04 7.473835e-01 7.473835e-01 1.283902e-03 1.955626e-04
## 6785 6815 6821 6857 6860 6869
## 1.851816e-04 9.728658e-01 7.473835e-01 3.671491e-05 7.473835e-01 8.693217e-01
## 6876 6880 6901 6914 6918 6920
## 9.087979e-03 1.923103e-04 1.283902e-03 1.077222e-03 7.570669e-08 7.473835e-01
## 6938 6939 6943 6946 6949 6955
## 1.851816e-04 8.051461e-03 1.851816e-04 1.851816e-04 7.473835e-01 8.309411e-02
## 6962 7026 7041 7048 7082 7103
## 1.955626e-04 7.473835e-01 1.283902e-03 7.473835e-01 7.473835e-01 7.473835e-01
## 7109 7116 7129 7137 7194 7202
## 7.865246e-06 1.113006e-05 7.473835e-01 1.361263e-05 1.851816e-04 7.473835e-01
## 7242 7261 7277 7295 7313 7316
## 7.473835e-01 1.851816e-04 9.087979e-03 1.292210e-02 1.283902e-03 1.851816e-04
## 7335 7337 7345 7352 7367 7373
## 1.851816e-04 1.384216e-02 1.113006e-05 1.851816e-04 7.473835e-01 1.716172e-01
## 7375 7392 7397 7398 7401 7430
## 7.473835e-01 7.473835e-01 1.113006e-05 1.851816e-04 1.283902e-03 3.124682e-02
## 7456 7458 7461 7472 7480 7482
## 1.283902e-03 3.671491e-05 8.309411e-02 5.317688e-05 7.473835e-01 1.851816e-04
## 7483 7508 7514 7564 7566 7569
## 1.947474e-04 1.851816e-04 7.473835e-01 3.094893e-02 8.309411e-02 1.292577e-02
## 7578 7619 7623 7624 7649 7697
## 1.851816e-04 7.473835e-01 7.473835e-01 1.980357e-04 1.361263e-05 1.283902e-03
## 7718 7731 7746 7756 7762 7765
## 1.851816e-04 1.851816e-04 3.671491e-05 1.716172e-01 4.206519e-05 8.309411e-02
## 7772 7779 7825 7830 7837 7850
## 1.415560e-01 7.473835e-01 3.342656e-05 1.113006e-05 8.309411e-02 1.361263e-05
## 7859 7862 7887 7890 7902 7904
## 3.671491e-05 3.342656e-05 1.851816e-04 1.361263e-05 1.252681e-01 7.473835e-01
## 7905 7941 7962 7964 7966 7983
## 1.851816e-04 1.851816e-04 3.124682e-02 1.283902e-03 8.309411e-02 1.851816e-04
## 8006 8015 8030 8077 8113 8123
## 1.283902e-03 1.851816e-04 3.342656e-05 1.851816e-04 1.851816e-04 8.693217e-01
## 8133 8134 8168 8174 8181 8183
## 8.309411e-02 1.177450e-01 3.671491e-05 1.252681e-01 7.865246e-06 1.851816e-04
## 8208 8220 8223 8253 8267 8293
## 1.851816e-04 7.473835e-01 7.473835e-01 3.671491e-05 1.259357e-05 1.851816e-04
## 8294 8309 8338 8342 8362 8365
## 7.473835e-01 1.260691e-01 8.309411e-02 1.851816e-04 1.113006e-05 7.865246e-06
## 8370 8373 8382 8393 8413 8426
## 6.987382e-05 8.309411e-02 1.851816e-04 1.292210e-02 1.283902e-03 8.309411e-02
## 8485 8542 8546 8566 8593 8609
## 7.473835e-01 1.851816e-04 7.473835e-01 8.309411e-02 7.473835e-01 1.716172e-01
## 8627 8663 8679 8708 8742 8746
## 3.192423e-06 3.124682e-02 7.865246e-06 3.342656e-05 1.283902e-03 1.252681e-01
## 8751 8761 8783 8787 8798 8825
## 2.210498e-04 1.851816e-04 1.292210e-02 1.292210e-02 1.851816e-04 8.309411e-02
## 8831 8850 8868 8878 8880 8894
## 1.851816e-04 1.283902e-03 7.473835e-01 2.464828e-05 9.255031e-02 8.309411e-02
## 8914 8931 8939 8953 8957 8992
## 3.671491e-05 1.851816e-04 3.671491e-05 1.283902e-03 1.292210e-02 8.693217e-01
## 8997 9015 9018 9025 9048 9061
## 8.309411e-02 7.473835e-01 1.534919e-06 7.473835e-01 1.252681e-01 1.292210e-02
## 9095 9098 9117 9129 9140 9163
## 1.851816e-04 8.273087e-01 1.851816e-04 2.153154e-04 9.131307e-02 1.113006e-05
## 9204 9210 9228 9229 9250 9253
## 7.473835e-01 7.473835e-01 1.851816e-04 1.851816e-04 9.255031e-02 8.309411e-02
## 9309 9310 9320 9341 9350 9355
## 3.124682e-02 7.473835e-01 2.829945e-02 7.473835e-01 8.309411e-02 7.473835e-01
## 9358 9373 9384 9407 9413 9424
## 1.426783e-05 1.283902e-03 3.124682e-02 1.361263e-05 3.671491e-05 1.851816e-04
## 9481 9486 9500 9553 9568 9570
## 1.292210e-02 1.283902e-03 1.283902e-03 1.252681e-01 1.851816e-04 1.361263e-05
## 9596 9607 9619 9641 9652 9659
## 7.865246e-06 3.899759e-07 1.851816e-04 1.361263e-05 1.252681e-01 8.967309e-01
## 9661 9664 9679 9682 9684 9714
## 7.473835e-01 1.113006e-05 6.987382e-05 7.473835e-01 1.851816e-04 5.317688e-05
## 9726 9763 9766 9782 9785 9792
## 2.337187e-04 2.337187e-04 7.865246e-06 8.048715e-03 7.473835e-01 1.361263e-05
## 9794 9809 9816 9834 9848 9851
## 7.473835e-01 7.865246e-06 1.851816e-04 1.851816e-04 1.177450e-01 1.113006e-05
## 9857 9877 9895 9905 9920 10003
## 8.441843e-07 7.473835e-01 8.048715e-03 1.851816e-04 7.865246e-06 1.851816e-04
## 10012 10054 10105 10110 10117 10118
## 1.851816e-04 1.139098e-01 1.876553e-04 3.342656e-05 1.283902e-03 8.048715e-03
## 10130 10155 10178 10201 10206 10209
## 7.473835e-01 7.473835e-01 9.947152e-02 7.473835e-01 1.964392e-04 7.473835e-01
## 10212 10241 10262 10270 10279 10290
## 1.632376e-06 2.153154e-04 7.865246e-06 7.473835e-01 3.671491e-05 1.851816e-04
## 10303 10364 10371 10386 10404 10423
## 1.851816e-04 1.955626e-04 7.473835e-01 1.980591e-04 9.656638e-02 1.361263e-05
## 10439 10449 10475 10480 10487 10500
## 1.851816e-04 8.309411e-02 7.784107e-07 1.361263e-05 1.851816e-04 7.473835e-01
## 10512 10516 10527 10544 10549 10568
## 1.292210e-02 3.124682e-02 7.473835e-01 8.309411e-02 1.851816e-04 8.309411e-02
## 10572 10582 10590 10638 10650 10694
## 1.851816e-04 1.361263e-05 8.309411e-02 5.188210e-04 7.753625e-01 3.124682e-02
## 10708 10724 10730 10736 10759 10771
## 1.384216e-02 3.671491e-05 7.473835e-01 1.851816e-04 7.473835e-01 1.292210e-02
## 10774 10776 10782 10802 10808 10836
## 7.473835e-01 1.113006e-05 1.283902e-03 7.473835e-01 1.851816e-04 8.568722e-03
## 10862 10874 10882 10886 10891 10906
## 7.473835e-01 1.252681e-01 8.309411e-02 5.317688e-05 2.464828e-05 7.473835e-01
## 10920 10929 10944 10952 10974 10995
## 8.309411e-02 7.473835e-01 7.473835e-01 7.865246e-06 8.693217e-01 1.851816e-04
## 11011 11025 11044 11073 11074 11123
## 7.865246e-06 1.851816e-04 1.292210e-02 6.393403e-03 8.309411e-02 1.113006e-05
## 11132 11142 11146 11156 11164 11171
## 1.283902e-03 1.113006e-05 7.473835e-01 3.094893e-02 4.415451e-04 3.094893e-02
## 11178 11217 11225 11234 11257 11296
## 1.361263e-05 1.851816e-04 7.473835e-01 1.000000e+00 7.473835e-01 1.851816e-04
## 11302 11305 11342 11352 11388 11390
## 1.384216e-02 1.361263e-05 3.236004e-05 3.671491e-05 3.342656e-05 7.473835e-01
## 11396 11417 11443 11456 11457 11461
## 5.317688e-05 1.851816e-04 3.124682e-02 9.087979e-03 1.632376e-06 7.570669e-08
## 11504 11527 11559 11560 11592 11606
## 7.865246e-06 1.283902e-03 8.309411e-02 1.851816e-04 1.252681e-01 7.473835e-01
## 11608 11642 11664 11671 11678 11700
## 8.310226e-02 7.473835e-01 1.283902e-03 1.851816e-04 1.361263e-05 7.473835e-01
## 11704 11707 11728 11729 11735 11753
## 7.473835e-01 8.273087e-01 1.892283e-05 2.829945e-02 8.693217e-01 1.113006e-05
## 11757 11818 11820 11839 11843 11859
## 7.473835e-01 1.283902e-03 1.361263e-05 1.283902e-03 3.342656e-05 8.309411e-02
## 11862 11887 11896 11935 11980 11988
## 1.283902e-03 2.177805e-04 1.851816e-04 7.473835e-01 1.384216e-02 7.473835e-01
## 12016 12030 12032 12049 12051 12075
## 8.309411e-02 7.473835e-01 1.851816e-04 8.309411e-02 3.342656e-05 1.851816e-04
## 12084 12091 12109 12114 12117 12128
## 1.851816e-04 7.473835e-01 2.050135e-04 1.283902e-03 3.671491e-05 5.317688e-05
## 12147 12160 12166 12190 12216 12236
## 8.693217e-01 1.283902e-03 8.568722e-03 1.851816e-04 8.693217e-01 8.309411e-02
## 12283 12307 12335 12367 12372 12376
## 1.252681e-01 3.342656e-05 8.693217e-01 1.851816e-04 5.317688e-05 1.851816e-04
## 12380 12382 12389 12395 12397 12408
## 8.309411e-02 3.671491e-05 1.252681e-01 1.252681e-01 8.309411e-02 7.473835e-01
## 12427 12439 12450 12452 12465 12468
## 1.851816e-04 9.087979e-03 1.283902e-03 1.283902e-03 7.473835e-01 1.361263e-05
## 12490 12496 12522 12527 12528 12537
## 8.309411e-02 8.309411e-02 7.865246e-06 1.113006e-05 7.473835e-01 7.473835e-01
## 12546 12550 12552 12554 12561 12568
## 7.865246e-06 1.851816e-04 1.851816e-04 1.384216e-02 9.656638e-02 1.851816e-04
## 12570 12579 12593 12607 12609 12622
## 7.473835e-01 7.473835e-01 3.124682e-02 7.473835e-01 1.851816e-04 3.671491e-05
## 12648 12705 12707 12739 12751 12761
## 1.851816e-04 1.283902e-03 8.693217e-01 2.829945e-02 1.283902e-03 1.490503e-02
## 12782 12799 12804 12806 12816 12843
## 1.283902e-03 7.473835e-01 7.865246e-06 7.473835e-01 3.192423e-06 3.671491e-05
## 12887 12888 12894 12901 12906 12918
## 7.473835e-01 7.473835e-01 1.851816e-04 1.923103e-04 7.473835e-01 3.671491e-05
## 12933 12943 12948 12956 12964 12968
## 1.363611e-05 1.113006e-05 2.337187e-04 9.947152e-02 1.283902e-03 7.473835e-01
## 12980 12984 13000 13035 13052 13087
## 1.851816e-04 7.473835e-01 8.693217e-01 7.473835e-01 1.283902e-03 1.716172e-01
## 13092 13104 13111 13133 13136 13137
## 1.851816e-04 1.851816e-04 8.309411e-02 1.851816e-04 1.415560e-01 1.283902e-03
## 13138 13143 13150 13154 13159 13188
## 1.384216e-02 7.473835e-01 1.292210e-02 1.361263e-05 1.851816e-04 7.473835e-01
## 13252 13261 13263 13284 13335 13336
## 7.473835e-01 1.851816e-04 6.393403e-03 1.361263e-05 1.361263e-05 7.473835e-01
## 13341 13360 13396 13450 13469 13497
## 1.851816e-04 8.309411e-02 7.473835e-01 7.473835e-01 3.192423e-06 1.361263e-05
## 13540 13553 13565 13586 13602 13621
## 1.876553e-04 1.851816e-04 1.384216e-02 7.865246e-06 8.309411e-02 1.361263e-05
## 13635 13636 13647 13656 13660 13674
## 1.632376e-06 1.292210e-02 7.473835e-01 1.361263e-05 3.671491e-05 7.473835e-01
## 13675 13682 13690 13732 13737 13742
## 7.473835e-01 1.283902e-03 1.361263e-05 7.473835e-01 3.342656e-05 8.309411e-02
## 13753 13755 13761 13777 13778 13782
## 7.473835e-01 1.851816e-04 1.851816e-04 2.051463e-04 1.384216e-02 8.309411e-02
## 13784 13791 13807 13892 13916 13962
## 1.851816e-04 1.283902e-03 7.473835e-01 7.473835e-01 1.077222e-03 7.473835e-01
## 13967 13989 14009 14035 14037 14060
## 1.851816e-04 1.851816e-04 1.113006e-05 7.473835e-01 1.851816e-04 1.851816e-04
## 14061 14064 14085 14095 14104 14110
## 1.252681e-01 9.087979e-03 7.473835e-01 8.309411e-02 5.317688e-05 7.865246e-06
## 14118 14119 14122 14125 14135 14137
## 1.361263e-05 2.177740e-04 7.473835e-01 7.473835e-01 7.473835e-01 1.851816e-04
## 14158 14187 14203 14204 14210 14216
## 7.865246e-06 1.851816e-04 8.309411e-02 1.440165e-05 7.473835e-01 3.342656e-05
## 14221 14256 14305 14311 14318 14321
## 7.865246e-06 8.568722e-03 7.473835e-01 9.095206e-03 7.473835e-01 1.851816e-04
## 14346 14358 14362 14368 14374 14403
## 2.402165e-01 8.309411e-02 1.851816e-04 1.851816e-04 1.283902e-03 1.851816e-04
## 14404 14414 14422 14435 14443 14481
## 9.479464e-01 1.851816e-04 7.473835e-01 7.473835e-01 3.671491e-05 1.283902e-03
## 14543 14571 14581 14592 14609 14628
## 7.473835e-01 8.048715e-03 1.252681e-01 7.473835e-01 8.406202e-07 7.473835e-01
## 14633 14635 14664 14683 14692 14725
## 3.671491e-05 8.309411e-02 7.473835e-01 5.317688e-05 8.309411e-02 1.851816e-04
## 14741 14743 14747 14751 14762 14780
## 1.851816e-04 7.473835e-01 1.851816e-04 7.473835e-01 8.693217e-01 1.851816e-04
## 14781 14787 14817 14819 14860 14895
## 4.817649e-04 7.473835e-01 1.283902e-03 1.851816e-04 5.317688e-05 7.865246e-06
## 14907 14925 14945 14947 14955 14963
## 7.473835e-01 1.851816e-04 7.473835e-01 3.124682e-02 1.361263e-05 2.139593e-05
## 15005 15024 15037 15096 15134 15143
## 2.674842e-01 1.851816e-04 1.851816e-04 7.473835e-01 7.865246e-06 1.415560e-01
## 15194 15196 15200 15217 15220 15242
## 8.309411e-02 3.671491e-05 7.865246e-06 6.987382e-05 1.361263e-05 3.342656e-05
## 15249
## 7.473835e-01hist(authority, labels = TRUE, ylim = c(0,800), xlim = c(0,1), xlab = "Authority Score", main = "Histograma de los Valores de Authority Score")
Cuales son los usuarios con authority score mayor a 0.8:
which(authority >= 0.8)## 134 679 1175 1467 1833 2316 2329 2426 2427 2660 2846 3009 3200
## 15 56 90 110 130 157 158 169 170 194 206 217 236
## 3265 3516 4010 4730 5049 5714 5846 5920 5996 6630 6815 6869 8123
## 239 259 289 331 360 404 409 423 430 472 482 486 576
## 8992 9098 9659 10974 11234 11707 11735 12147 12216 12335 12707 13000 14404
## 630 638 672 761 778 806 809 835 839 843 879 903 997
## 14762
## 1019colorfunc <- colorRampPalette(c( "red",
"firebrick",
"lightcoral",
"saddlebrown",
"tomato",
"darkorange",
"gold",
"lightgreen",
"mediumspringgreen",
"dodgerblue",
"steelblue"))
scaled_authority = authority *1000000 + 1
colors <- colorfunc(max(scaled_authority))
V(users_projected)$color <- colors[scaled_authority]
V(users_projected)$shape <- "circle"
E(users_projected)$color <- "lightgray"
plot(users_projected,
vertex.label = format(round(authority, 2), nsmall = 1),
vertex.label.color = "Black",
vertex.size = 3,
edge.arrow.size=0.1,
layout = layout_components,
main = "Proyección Red Usuarios: Nodos según Authority Score")
plot(users_projected,
vertex.label = NA,
vertex.label.color = "Black",
vertex.size = 3,
edge.arrow.size=0.1,
layout = layout_components,
main = "Proyección Red Usuarios: Nodos según Authority Score")
Hubs
hubs = hub.score(users_projected)$vector
hubs## 10 11 41 51 114 67
## 1.851816e-04 1.283902e-03 3.094893e-02 8.309411e-02 8.051461e-03 7.473835e-01
## 78 82 85 98 107 128
## 1.361263e-05 1.320908e-03 3.240013e-04 7.473835e-01 1.851816e-04 5.317688e-05
## 132 133 134 143 144 169
## 1.632376e-06 8.568722e-03 8.693217e-01 1.384216e-02 3.094893e-02 1.851816e-04
## 182 187 189 203 231 234
## 1.252681e-01 7.865246e-06 7.473835e-01 8.309411e-02 8.309411e-02 1.851816e-04
## 246 254 257 258 281 306
## 1.361263e-05 1.506834e-02 1.283902e-03 1.955626e-04 7.473835e-01 7.865246e-06
## 345 349 354 376 393 398
## 1.283902e-03 1.283902e-03 7.473835e-01 3.124682e-02 1.113006e-05 1.283902e-03
## 448 456 466 470 471 478
## 7.473835e-01 1.632376e-06 2.210498e-04 7.473835e-01 1.292210e-02 8.309411e-02
## 499 519 521 522 529 533
## 1.283902e-03 2.829945e-02 8.309411e-02 1.716172e-01 8.309411e-02 1.283902e-03
## 568 595 620 639 648 656
## 2.177740e-04 3.192423e-06 7.473835e-01 1.283902e-03 3.124682e-02 1.851816e-04
## 657 679 697 743 771 796
## 3.914832e-02 8.693217e-01 1.361263e-05 1.384216e-02 1.716172e-01 1.283902e-03
## 822 837 838 840 841 859
## 1.283902e-03 1.384216e-02 3.671491e-05 7.473835e-01 1.632376e-06 8.309411e-02
## 861 873 917 940 946 948
## 1.283902e-03 1.283902e-03 7.473835e-01 2.196020e-01 1.283902e-03 1.292210e-02
## 968 973 976 994 1000 1002
## 1.376629e-01 1.851816e-04 7.865246e-06 7.473835e-01 7.473835e-01 1.252681e-01
## 1024 1031 1032 1039 1055 1076
## 1.851816e-04 3.671491e-05 5.317688e-05 1.851816e-04 1.384216e-02 7.473835e-01
## 1102 1110 1138 1163 1172 1175
## 1.851816e-04 9.947152e-02 1.851816e-04 1.923103e-04 1.851816e-04 8.693217e-01
## 1209 1214 1218 1223 1224 1257
## 8.309411e-02 1.851816e-04 7.473835e-01 1.851816e-04 7.473835e-01 1.851816e-04
## 1258 1280 1307 1313 1329 1362
## 7.865246e-06 1.851816e-04 1.147608e-05 1.252681e-01 1.851816e-04 8.051461e-03
## 1412 1415 1416 1424 1429 1445
## 7.473835e-01 8.309411e-02 7.473835e-01 8.309411e-02 3.094893e-02 3.124682e-02
## 1462 1467 1481 1483 1494 1507
## 3.094893e-02 8.693217e-01 8.309411e-02 3.671491e-05 1.252681e-01 7.473835e-01
## 1544 1559 1596 1598 1639 1657
## 1.716172e-01 1.016310e-01 7.473835e-01 3.094893e-02 1.361263e-05 8.568722e-03
## 1660 1705 1709 1718 1739 1752
## 7.473835e-01 1.851816e-04 8.309411e-02 5.977311e-06 1.361263e-05 8.309411e-02
## 1760 1790 1826 1833 1849 1884
## 1.361263e-05 1.283902e-03 4.206519e-05 8.273087e-01 7.473835e-01 1.361263e-05
## 1898 1902 1924 1933 1984 1994
## 1.716172e-01 8.309411e-02 1.283902e-03 1.851816e-04 1.851816e-04 1.851816e-04
## 2001 2011 2019 2026 2042 2063
## 1.283902e-03 1.361263e-05 7.865246e-06 7.473835e-01 6.573193e-03 5.317688e-05
## 2084 2129 2143 2149 2188 2192
## 7.473835e-01 7.473835e-01 7.473835e-01 7.473835e-01 9.183034e-02 1.283902e-03
## 2193 2199 2227 2250 2286 2293
## 7.473835e-01 3.671491e-05 2.177740e-04 5.317688e-05 1.851816e-04 8.309411e-02
## 2316 2329 2330 2335 2349 2350
## 8.693217e-01 8.693217e-01 1.283902e-03 1.361263e-05 1.252681e-01 7.473835e-01
## 2377 2380 2388 2393 2405 2415
## 3.124682e-02 7.473835e-01 1.851816e-04 1.361263e-05 1.384216e-02 8.309411e-02
## 2426 2427 2460 2462 2466 2471
## 9.484796e-01 9.216813e-01 3.671491e-05 7.473835e-01 1.292210e-02 7.473835e-01
## 2484 2491 2492 2502 2504 2516
## 1.851816e-04 1.252681e-01 1.851816e-04 1.851816e-04 8.309411e-02 7.865246e-06
## 2524 2526 2527 2533 2542 2549
## 1.361263e-05 1.892283e-05 7.473835e-01 3.899759e-07 7.473835e-01 1.851816e-04
## 2557 2562 2582 2596 2601 2603
## 5.317688e-05 9.183034e-02 1.632376e-06 2.205150e-01 1.851816e-04 8.309411e-02
## 2623 2660 2663 2701 2720 2730
## 1.113006e-05 8.693217e-01 1.951956e-01 1.113006e-05 7.865246e-06 1.283902e-03
## 2738 2795 2806 2811 2820 2835
## 1.283902e-03 7.473835e-01 6.423139e-04 7.473835e-01 1.534919e-06 7.473835e-01
## 2844 2846 2849 2861 2872 2876
## 7.473835e-01 8.693217e-01 7.473835e-01 8.568722e-03 4.415451e-04 8.309411e-02
## 2908 2920 2951 2961 2986 2987
## 1.851816e-04 1.851816e-04 3.124682e-02 1.361263e-05 8.309411e-02 1.113006e-05
## 3009 3020 3029 3051 3058 3065
## 8.693217e-01 1.252681e-01 7.473835e-01 1.851816e-04 1.283902e-03 7.473835e-01
## 3066 3091 3103 3119 3134 3136
## 1.851816e-04 7.473835e-01 1.113006e-05 7.473835e-01 1.384216e-02 7.865246e-06
## 3137 3150 3152 3158 3167 3186
## 1.292210e-02 7.473835e-01 8.318815e-05 2.075671e-01 1.113006e-05 7.473835e-01
## 3187 3200 3230 3260 3265 3278
## 1.441853e-01 8.788411e-01 1.851816e-04 3.859943e-03 8.967309e-01 1.283902e-03
## 3298 3314 3327 3345 3350 3356
## 7.473835e-01 7.473835e-01 7.473835e-01 7.473835e-01 1.851816e-04 3.342656e-05
## 3384 3397 3401 3404 3408 3411
## 7.473835e-01 9.679743e-02 7.865246e-06 1.361263e-05 7.473835e-01 1.361263e-05
## 3443 3459 3461 3469 3491 3505
## 7.473835e-01 7.473835e-01 3.192423e-06 8.309411e-02 3.671491e-05 1.283902e-03
## 3516 3519 3533 3549 3554 3557
## 8.551731e-01 2.177740e-04 1.851816e-04 7.865246e-06 8.309411e-02 7.473835e-01
## 3571 3577 3595 3596 3635 3648
## 1.851816e-04 1.361263e-05 1.361263e-05 8.309411e-02 1.252681e-01 7.473835e-01
## 3651 3723 3727 3728 3733 3750
## 8.309411e-02 1.851816e-04 2.464828e-05 8.309411e-02 3.342656e-05 1.851816e-04
## 3752 3753 3790 3833 3845 3857
## 8.309411e-02 1.252681e-01 1.851816e-04 7.473835e-01 2.829945e-02 1.283902e-03
## 3873 3893 3940 3965 3987 3989
## 9.815029e-03 1.851816e-04 1.361263e-05 3.124682e-02 3.671491e-05 1.283902e-03
## 4010 4011 4014 4068 4070 4080
## 8.693217e-01 1.113006e-05 7.473835e-01 1.376629e-01 7.473835e-01 3.192423e-06
## 4081 4102 4124 4159 4214 4229
## 7.473835e-01 1.851816e-04 5.317688e-05 1.851816e-04 3.124682e-02 7.473835e-01
## 4243 4245 4273 4276 4303 4304
## 7.473835e-01 3.124682e-02 7.473835e-01 1.283902e-03 1.851816e-04 8.309411e-02
## 4310 4323 4350 4352 4369 4374
## 1.851816e-04 7.473835e-01 3.859943e-03 3.899759e-07 7.473835e-01 3.342656e-05
## 4384 4432 4461 4470 4472 4476
## 1.980357e-04 7.473835e-01 5.317688e-05 1.283902e-03 1.851816e-04 1.851816e-04
## 4489 4526 4536 4542 4550 4590
## 1.955626e-04 7.473835e-01 1.851816e-04 1.283902e-03 1.632376e-06 1.012641e-02
## 4600 4615 4633 4667 4705 4708
## 1.851816e-04 3.124682e-02 7.473835e-01 7.473835e-01 1.113006e-05 1.851816e-04
## 4730 4750 4751 4756 4760 4792
## 8.693217e-01 1.851816e-04 3.342656e-05 3.342656e-05 7.473835e-01 8.309411e-02
## 4800 4801 4820 4822 4823 4833
## 6.393403e-03 7.865246e-06 8.309411e-02 1.851816e-04 1.632376e-06 1.851816e-04
## 4843 4846 4862 4864 4875 4879
## 9.947152e-02 1.851816e-04 1.113006e-05 7.473835e-01 3.342656e-05 5.317688e-05
## 4890 4904 4910 4921 4930 4954
## 1.283902e-03 3.671491e-05 7.473835e-01 8.309411e-02 2.089353e-02 3.671491e-05
## 4964 4982 5014 5015 5044 5049
## 3.342656e-05 3.671491e-05 3.094893e-02 1.851816e-04 1.113006e-05 8.693217e-01
## 5055 5057 5072 5103 5115 5120
## 1.892283e-05 7.473835e-01 7.473835e-01 5.317688e-05 1.716172e-01 8.405604e-02
## 5132 5136 5146 5179 5189 5223
## 3.192423e-06 8.980721e-02 1.361263e-05 1.851816e-04 1.361263e-05 7.473835e-01
## 5231 5251 5292 5293 5327 5331
## 1.851816e-04 3.124682e-02 3.124682e-02 1.851816e-04 1.851816e-04 1.283902e-03
## 5342 5359 5423 5424 5438 5446
## 7.473835e-01 3.671491e-05 7.473835e-01 3.671491e-05 7.473835e-01 1.361263e-05
## 5453 5461 5466 5507 5508 5531
## 8.309411e-02 6.393403e-03 1.964649e-04 1.892283e-05 8.385015e-02 7.473835e-01
## 5547 5552 5554 5556 5564 5579
## 1.851816e-04 2.210498e-04 1.283902e-03 7.473835e-01 7.865246e-06 8.406202e-07
## 5583 5597 5624 5666 5670 5688
## 7.473835e-01 3.342656e-05 1.851816e-04 1.851816e-04 1.851816e-04 1.851816e-04
## 5707 5714 5726 5731 5771 5793
## 1.851816e-04 8.693217e-01 7.473835e-01 1.283902e-03 7.473835e-01 1.851816e-04
## 5846 5849 5867 5881 5886 5899
## 8.693217e-01 7.473835e-01 1.851816e-04 1.851816e-04 1.292210e-02 1.361263e-05
## 5901 5902 5904 5905 5906 5911
## 1.851816e-04 1.851816e-04 1.851816e-04 1.292210e-02 7.473835e-01 8.309411e-02
## 5912 5918 5920 5939 5948 5962
## 1.283902e-03 1.851816e-04 8.693217e-01 1.113006e-05 1.361263e-05 1.955626e-04
## 5965 5973 5994 5996 5999 6003
## 7.473835e-01 7.473835e-01 1.851816e-04 9.221897e-01 1.113006e-05 1.851816e-04
## 6009 6018 6022 6054 6078 6085
## 9.947152e-02 1.980357e-04 3.671491e-05 1.994648e-01 6.987382e-05 1.851816e-04
## 6118 6121 6125 6134 6145 6163
## 7.473835e-01 7.473835e-01 8.309411e-02 7.865246e-06 3.342656e-05 7.473835e-01
## 6169 6173 6190 6202 6221 6223
## 7.865246e-06 2.829945e-02 1.851816e-04 3.192423e-06 1.283902e-03 7.473835e-01
## 6258 6322 6336 6345 6349 6350
## 7.473835e-01 1.283902e-03 1.361263e-05 9.087979e-03 3.671491e-05 7.865246e-06
## 6354 6367 6370 6373 6391 6404
## 1.283902e-03 1.851816e-04 1.851816e-04 1.292210e-02 1.851816e-04 1.283902e-03
## 6451 6472 6498 6505 6554 6563
## 7.473835e-01 1.851816e-04 1.851816e-04 7.473835e-01 7.473835e-01 1.283902e-03
## 6576 6591 6600 6630 6647 6679
## 8.309411e-02 1.955626e-04 3.124682e-02 8.273087e-01 9.087979e-03 1.851816e-04
## 6690 6695 6742 6757 6764 6784
## 7.865246e-06 1.851816e-04 7.473835e-01 7.473835e-01 1.283902e-03 1.955626e-04
## 6785 6815 6821 6857 6860 6869
## 1.851816e-04 9.728658e-01 7.473835e-01 3.671491e-05 7.473835e-01 8.693217e-01
## 6876 6880 6901 6914 6918 6920
## 9.087979e-03 1.923103e-04 1.283902e-03 1.077222e-03 7.570669e-08 7.473835e-01
## 6938 6939 6943 6946 6949 6955
## 1.851816e-04 8.051461e-03 1.851816e-04 1.851816e-04 7.473835e-01 8.309411e-02
## 6962 7026 7041 7048 7082 7103
## 1.955626e-04 7.473835e-01 1.283902e-03 7.473835e-01 7.473835e-01 7.473835e-01
## 7109 7116 7129 7137 7194 7202
## 7.865246e-06 1.113006e-05 7.473835e-01 1.361263e-05 1.851816e-04 7.473835e-01
## 7242 7261 7277 7295 7313 7316
## 7.473835e-01 1.851816e-04 9.087979e-03 1.292210e-02 1.283902e-03 1.851816e-04
## 7335 7337 7345 7352 7367 7373
## 1.851816e-04 1.384216e-02 1.113006e-05 1.851816e-04 7.473835e-01 1.716172e-01
## 7375 7392 7397 7398 7401 7430
## 7.473835e-01 7.473835e-01 1.113006e-05 1.851816e-04 1.283902e-03 3.124682e-02
## 7456 7458 7461 7472 7480 7482
## 1.283902e-03 3.671491e-05 8.309411e-02 5.317688e-05 7.473835e-01 1.851816e-04
## 7483 7508 7514 7564 7566 7569
## 1.947474e-04 1.851816e-04 7.473835e-01 3.094893e-02 8.309411e-02 1.292577e-02
## 7578 7619 7623 7624 7649 7697
## 1.851816e-04 7.473835e-01 7.473835e-01 1.980357e-04 1.361263e-05 1.283902e-03
## 7718 7731 7746 7756 7762 7765
## 1.851816e-04 1.851816e-04 3.671491e-05 1.716172e-01 4.206519e-05 8.309411e-02
## 7772 7779 7825 7830 7837 7850
## 1.415560e-01 7.473835e-01 3.342656e-05 1.113006e-05 8.309411e-02 1.361263e-05
## 7859 7862 7887 7890 7902 7904
## 3.671491e-05 3.342656e-05 1.851816e-04 1.361263e-05 1.252681e-01 7.473835e-01
## 7905 7941 7962 7964 7966 7983
## 1.851816e-04 1.851816e-04 3.124682e-02 1.283902e-03 8.309411e-02 1.851816e-04
## 8006 8015 8030 8077 8113 8123
## 1.283902e-03 1.851816e-04 3.342656e-05 1.851816e-04 1.851816e-04 8.693217e-01
## 8133 8134 8168 8174 8181 8183
## 8.309411e-02 1.177450e-01 3.671491e-05 1.252681e-01 7.865246e-06 1.851816e-04
## 8208 8220 8223 8253 8267 8293
## 1.851816e-04 7.473835e-01 7.473835e-01 3.671491e-05 1.259357e-05 1.851816e-04
## 8294 8309 8338 8342 8362 8365
## 7.473835e-01 1.260691e-01 8.309411e-02 1.851816e-04 1.113006e-05 7.865246e-06
## 8370 8373 8382 8393 8413 8426
## 6.987382e-05 8.309411e-02 1.851816e-04 1.292210e-02 1.283902e-03 8.309411e-02
## 8485 8542 8546 8566 8593 8609
## 7.473835e-01 1.851816e-04 7.473835e-01 8.309411e-02 7.473835e-01 1.716172e-01
## 8627 8663 8679 8708 8742 8746
## 3.192423e-06 3.124682e-02 7.865246e-06 3.342656e-05 1.283902e-03 1.252681e-01
## 8751 8761 8783 8787 8798 8825
## 2.210498e-04 1.851816e-04 1.292210e-02 1.292210e-02 1.851816e-04 8.309411e-02
## 8831 8850 8868 8878 8880 8894
## 1.851816e-04 1.283902e-03 7.473835e-01 2.464828e-05 9.255031e-02 8.309411e-02
## 8914 8931 8939 8953 8957 8992
## 3.671491e-05 1.851816e-04 3.671491e-05 1.283902e-03 1.292210e-02 8.693217e-01
## 8997 9015 9018 9025 9048 9061
## 8.309411e-02 7.473835e-01 1.534919e-06 7.473835e-01 1.252681e-01 1.292210e-02
## 9095 9098 9117 9129 9140 9163
## 1.851816e-04 8.273087e-01 1.851816e-04 2.153154e-04 9.131307e-02 1.113006e-05
## 9204 9210 9228 9229 9250 9253
## 7.473835e-01 7.473835e-01 1.851816e-04 1.851816e-04 9.255031e-02 8.309411e-02
## 9309 9310 9320 9341 9350 9355
## 3.124682e-02 7.473835e-01 2.829945e-02 7.473835e-01 8.309411e-02 7.473835e-01
## 9358 9373 9384 9407 9413 9424
## 1.426783e-05 1.283902e-03 3.124682e-02 1.361263e-05 3.671491e-05 1.851816e-04
## 9481 9486 9500 9553 9568 9570
## 1.292210e-02 1.283902e-03 1.283902e-03 1.252681e-01 1.851816e-04 1.361263e-05
## 9596 9607 9619 9641 9652 9659
## 7.865246e-06 3.899759e-07 1.851816e-04 1.361263e-05 1.252681e-01 8.967309e-01
## 9661 9664 9679 9682 9684 9714
## 7.473835e-01 1.113006e-05 6.987382e-05 7.473835e-01 1.851816e-04 5.317688e-05
## 9726 9763 9766 9782 9785 9792
## 2.337187e-04 2.337187e-04 7.865246e-06 8.048715e-03 7.473835e-01 1.361263e-05
## 9794 9809 9816 9834 9848 9851
## 7.473835e-01 7.865246e-06 1.851816e-04 1.851816e-04 1.177450e-01 1.113006e-05
## 9857 9877 9895 9905 9920 10003
## 8.441843e-07 7.473835e-01 8.048715e-03 1.851816e-04 7.865246e-06 1.851816e-04
## 10012 10054 10105 10110 10117 10118
## 1.851816e-04 1.139098e-01 1.876553e-04 3.342656e-05 1.283902e-03 8.048715e-03
## 10130 10155 10178 10201 10206 10209
## 7.473835e-01 7.473835e-01 9.947152e-02 7.473835e-01 1.964392e-04 7.473835e-01
## 10212 10241 10262 10270 10279 10290
## 1.632376e-06 2.153154e-04 7.865246e-06 7.473835e-01 3.671491e-05 1.851816e-04
## 10303 10364 10371 10386 10404 10423
## 1.851816e-04 1.955626e-04 7.473835e-01 1.980591e-04 9.656638e-02 1.361263e-05
## 10439 10449 10475 10480 10487 10500
## 1.851816e-04 8.309411e-02 7.784107e-07 1.361263e-05 1.851816e-04 7.473835e-01
## 10512 10516 10527 10544 10549 10568
## 1.292210e-02 3.124682e-02 7.473835e-01 8.309411e-02 1.851816e-04 8.309411e-02
## 10572 10582 10590 10638 10650 10694
## 1.851816e-04 1.361263e-05 8.309411e-02 5.188210e-04 7.753625e-01 3.124682e-02
## 10708 10724 10730 10736 10759 10771
## 1.384216e-02 3.671491e-05 7.473835e-01 1.851816e-04 7.473835e-01 1.292210e-02
## 10774 10776 10782 10802 10808 10836
## 7.473835e-01 1.113006e-05 1.283902e-03 7.473835e-01 1.851816e-04 8.568722e-03
## 10862 10874 10882 10886 10891 10906
## 7.473835e-01 1.252681e-01 8.309411e-02 5.317688e-05 2.464828e-05 7.473835e-01
## 10920 10929 10944 10952 10974 10995
## 8.309411e-02 7.473835e-01 7.473835e-01 7.865246e-06 8.693217e-01 1.851816e-04
## 11011 11025 11044 11073 11074 11123
## 7.865246e-06 1.851816e-04 1.292210e-02 6.393403e-03 8.309411e-02 1.113006e-05
## 11132 11142 11146 11156 11164 11171
## 1.283902e-03 1.113006e-05 7.473835e-01 3.094893e-02 4.415451e-04 3.094893e-02
## 11178 11217 11225 11234 11257 11296
## 1.361263e-05 1.851816e-04 7.473835e-01 1.000000e+00 7.473835e-01 1.851816e-04
## 11302 11305 11342 11352 11388 11390
## 1.384216e-02 1.361263e-05 3.236004e-05 3.671491e-05 3.342656e-05 7.473835e-01
## 11396 11417 11443 11456 11457 11461
## 5.317688e-05 1.851816e-04 3.124682e-02 9.087979e-03 1.632376e-06 7.570669e-08
## 11504 11527 11559 11560 11592 11606
## 7.865246e-06 1.283902e-03 8.309411e-02 1.851816e-04 1.252681e-01 7.473835e-01
## 11608 11642 11664 11671 11678 11700
## 8.310226e-02 7.473835e-01 1.283902e-03 1.851816e-04 1.361263e-05 7.473835e-01
## 11704 11707 11728 11729 11735 11753
## 7.473835e-01 8.273087e-01 1.892283e-05 2.829945e-02 8.693217e-01 1.113006e-05
## 11757 11818 11820 11839 11843 11859
## 7.473835e-01 1.283902e-03 1.361263e-05 1.283902e-03 3.342656e-05 8.309411e-02
## 11862 11887 11896 11935 11980 11988
## 1.283902e-03 2.177805e-04 1.851816e-04 7.473835e-01 1.384216e-02 7.473835e-01
## 12016 12030 12032 12049 12051 12075
## 8.309411e-02 7.473835e-01 1.851816e-04 8.309411e-02 3.342656e-05 1.851816e-04
## 12084 12091 12109 12114 12117 12128
## 1.851816e-04 7.473835e-01 2.050135e-04 1.283902e-03 3.671491e-05 5.317688e-05
## 12147 12160 12166 12190 12216 12236
## 8.693217e-01 1.283902e-03 8.568722e-03 1.851816e-04 8.693217e-01 8.309411e-02
## 12283 12307 12335 12367 12372 12376
## 1.252681e-01 3.342656e-05 8.693217e-01 1.851816e-04 5.317688e-05 1.851816e-04
## 12380 12382 12389 12395 12397 12408
## 8.309411e-02 3.671491e-05 1.252681e-01 1.252681e-01 8.309411e-02 7.473835e-01
## 12427 12439 12450 12452 12465 12468
## 1.851816e-04 9.087979e-03 1.283902e-03 1.283902e-03 7.473835e-01 1.361263e-05
## 12490 12496 12522 12527 12528 12537
## 8.309411e-02 8.309411e-02 7.865246e-06 1.113006e-05 7.473835e-01 7.473835e-01
## 12546 12550 12552 12554 12561 12568
## 7.865246e-06 1.851816e-04 1.851816e-04 1.384216e-02 9.656638e-02 1.851816e-04
## 12570 12579 12593 12607 12609 12622
## 7.473835e-01 7.473835e-01 3.124682e-02 7.473835e-01 1.851816e-04 3.671491e-05
## 12648 12705 12707 12739 12751 12761
## 1.851816e-04 1.283902e-03 8.693217e-01 2.829945e-02 1.283902e-03 1.490503e-02
## 12782 12799 12804 12806 12816 12843
## 1.283902e-03 7.473835e-01 7.865246e-06 7.473835e-01 3.192423e-06 3.671491e-05
## 12887 12888 12894 12901 12906 12918
## 7.473835e-01 7.473835e-01 1.851816e-04 1.923103e-04 7.473835e-01 3.671491e-05
## 12933 12943 12948 12956 12964 12968
## 1.363611e-05 1.113006e-05 2.337187e-04 9.947152e-02 1.283902e-03 7.473835e-01
## 12980 12984 13000 13035 13052 13087
## 1.851816e-04 7.473835e-01 8.693217e-01 7.473835e-01 1.283902e-03 1.716172e-01
## 13092 13104 13111 13133 13136 13137
## 1.851816e-04 1.851816e-04 8.309411e-02 1.851816e-04 1.415560e-01 1.283902e-03
## 13138 13143 13150 13154 13159 13188
## 1.384216e-02 7.473835e-01 1.292210e-02 1.361263e-05 1.851816e-04 7.473835e-01
## 13252 13261 13263 13284 13335 13336
## 7.473835e-01 1.851816e-04 6.393403e-03 1.361263e-05 1.361263e-05 7.473835e-01
## 13341 13360 13396 13450 13469 13497
## 1.851816e-04 8.309411e-02 7.473835e-01 7.473835e-01 3.192423e-06 1.361263e-05
## 13540 13553 13565 13586 13602 13621
## 1.876553e-04 1.851816e-04 1.384216e-02 7.865246e-06 8.309411e-02 1.361263e-05
## 13635 13636 13647 13656 13660 13674
## 1.632376e-06 1.292210e-02 7.473835e-01 1.361263e-05 3.671491e-05 7.473835e-01
## 13675 13682 13690 13732 13737 13742
## 7.473835e-01 1.283902e-03 1.361263e-05 7.473835e-01 3.342656e-05 8.309411e-02
## 13753 13755 13761 13777 13778 13782
## 7.473835e-01 1.851816e-04 1.851816e-04 2.051463e-04 1.384216e-02 8.309411e-02
## 13784 13791 13807 13892 13916 13962
## 1.851816e-04 1.283902e-03 7.473835e-01 7.473835e-01 1.077222e-03 7.473835e-01
## 13967 13989 14009 14035 14037 14060
## 1.851816e-04 1.851816e-04 1.113006e-05 7.473835e-01 1.851816e-04 1.851816e-04
## 14061 14064 14085 14095 14104 14110
## 1.252681e-01 9.087979e-03 7.473835e-01 8.309411e-02 5.317688e-05 7.865246e-06
## 14118 14119 14122 14125 14135 14137
## 1.361263e-05 2.177740e-04 7.473835e-01 7.473835e-01 7.473835e-01 1.851816e-04
## 14158 14187 14203 14204 14210 14216
## 7.865246e-06 1.851816e-04 8.309411e-02 1.440165e-05 7.473835e-01 3.342656e-05
## 14221 14256 14305 14311 14318 14321
## 7.865246e-06 8.568722e-03 7.473835e-01 9.095206e-03 7.473835e-01 1.851816e-04
## 14346 14358 14362 14368 14374 14403
## 2.402165e-01 8.309411e-02 1.851816e-04 1.851816e-04 1.283902e-03 1.851816e-04
## 14404 14414 14422 14435 14443 14481
## 9.479464e-01 1.851816e-04 7.473835e-01 7.473835e-01 3.671491e-05 1.283902e-03
## 14543 14571 14581 14592 14609 14628
## 7.473835e-01 8.048715e-03 1.252681e-01 7.473835e-01 8.406202e-07 7.473835e-01
## 14633 14635 14664 14683 14692 14725
## 3.671491e-05 8.309411e-02 7.473835e-01 5.317688e-05 8.309411e-02 1.851816e-04
## 14741 14743 14747 14751 14762 14780
## 1.851816e-04 7.473835e-01 1.851816e-04 7.473835e-01 8.693217e-01 1.851816e-04
## 14781 14787 14817 14819 14860 14895
## 4.817649e-04 7.473835e-01 1.283902e-03 1.851816e-04 5.317688e-05 7.865246e-06
## 14907 14925 14945 14947 14955 14963
## 7.473835e-01 1.851816e-04 7.473835e-01 3.124682e-02 1.361263e-05 2.139593e-05
## 15005 15024 15037 15096 15134 15143
## 2.674842e-01 1.851816e-04 1.851816e-04 7.473835e-01 7.865246e-06 1.415560e-01
## 15194 15196 15200 15217 15220 15242
## 8.309411e-02 3.671491e-05 7.865246e-06 6.987382e-05 1.361263e-05 3.342656e-05
## 15249
## 7.473835e-01hist(hubs, labels = TRUE, ylim = c(0,800), xlim = c(0,1), xlab = "Hub Score", main = "Histograma de los Valores de Hub Score")
Cuales son los usuarios con alto hub score:
which(hubs >= 0.8)## 134 679 1175 1467 1833 2316 2329 2426 2427 2660 2846 3009 3200
## 15 56 90 110 130 157 158 169 170 194 206 217 236
## 3265 3516 4010 4730 5049 5714 5846 5920 5996 6630 6815 6869 8123
## 239 259 289 331 360 404 409 423 430 472 482 486 576
## 8992 9098 9659 10974 11234 11707 11735 12147 12216 12335 12707 13000 14404
## 630 638 672 761 778 806 809 835 839 843 879 903 997
## 14762
## 1019colorfunc <- colorRampPalette(c( "red",
"firebrick",
"lightcoral",
"saddlebrown",
"tomato",
"darkorange",
"gold",
"lightgreen",
"mediumspringgreen",
"dodgerblue",
"steelblue"))
scaled_hubs = hubs * 1000000 + 1
colors <- colorfunc(max(scaled_hubs))
V(users_projected)$color <- colors[scaled_hubs]
V(users_projected)$shape <- "circle"
E(users_projected)$color <- "lightgray"
plot(users_projected,
vertex.label = format(round(hubs, 2), nsmall = 1),
vertex.label.color = "Black",
vertex.size = 3,
edge.arrow.size=0.1,
layout = layout_components,
main = "Proyección Red Usuarios: Nodos según Hub Score")
plot(users_projected,
vertex.label = NA,
vertex.label.color = "Black",
vertex.size = 3,
edge.arrow.size=0.1,
layout = layout_components,
main = "Proyección Red Usuarios: Nodos según Hub Score")
Medidas de Centralidad Basadas en Caminos más Cortos
Closeness Centrality
closeness_centrality = closeness(users_projected)
closeness_centrality## 10 11 41 51 114 67
## 0.0003838772 0.0002992220 0.0003567606 0.0003933910 0.0003539823 0.0003875969
## 78 82 85 98 107 128
## 0.0003176620 0.0003001200 0.0003620565 0.0003875969 0.0003838772 0.0002642706
## 132 133 134 143 144 169
## 0.0002843332 0.0003601008 0.0003998401 0.0003639010 0.0003567606 0.0003838772
## 182 187 189 203 231 234
## 0.0003714710 0.0002913753 0.0003875969 0.0003933910 0.0003933910 0.0003838772
## 246 254 257 258 281 306
## 0.0003176620 0.0003779289 0.0002986858 0.0003903201 0.0003875969 0.0002913753
## 345 349 354 376 393 398
## 0.0002986858 0.0002986858 0.0003875969 0.0003610108 0.0002922268 0.0002986858
## 448 456 466 470 471 478
## 0.0003875969 0.0002843332 0.0003408316 0.0003875969 0.0003577818 0.0003933910
## 499 519 521 522 529 533
## 0.0002986858 0.0003575259 0.0003933910 0.0003941663 0.0003933910 0.0002986858
## 568 595 620 639 648 656
## 0.0003359086 0.0002856327 0.0003875969 0.0002986858 0.0003610108 0.0003838772
## 657 679 697 743 771 796
## 0.0003627131 0.0003998401 0.0003176620 0.0003639010 0.0003941663 0.0002986858
## 822 837 838 840 841 859
## 0.0002986858 0.0003639010 0.0003498950 0.0003875969 0.0002843332 0.0003933910
## 861 873 917 940 946 948
## 0.0002986858 0.0002986858 0.0003875969 0.0004050223 0.0002986858 0.0003581662
## 968 973 976 994 1000 1002
## 0.0003772161 0.0003838772 0.0002913753 0.0003875969 0.0003875969 0.0003714710
## 1024 1031 1032 1039 1055 1076
## 0.0003838772 0.0003498950 0.0002642706 0.0003838772 0.0003639010 0.0003875969
## 1102 1110 1138 1163 1172 1175
## 0.0003838772 0.0003920031 0.0003838772 0.0003938558 0.0003838772 0.0003998401
## 1209 1214 1218 1223 1224 1257
## 0.0003935458 0.0003838772 0.0003875969 0.0003838772 0.0003875969 0.0003838772
## 1258 1280 1307 1313 1329 1362
## 0.0002913753 0.0003838772 0.0002968240 0.0003714710 0.0003838772 0.0003539823
## 1412 1415 1416 1424 1429 1445
## 0.0003875969 0.0003935458 0.0003875969 0.0003935458 0.0003567606 0.0003610108
## 1462 1467 1481 1483 1494 1507
## 0.0003567606 0.0003998401 0.0003935458 0.0003498950 0.0003714710 0.0003875969
## 1544 1559 1596 1598 1639 1657
## 0.0003941663 0.0004633920 0.0003875969 0.0003567606 0.0003176620 0.0003601008
## 1660 1705 1709 1718 1739 1752
## 0.0003875969 0.0003838772 0.0003933910 0.0002958580 0.0003176620 0.0003933910
## 1760 1790 1826 1833 1849 1884
## 0.0003176620 0.0002986858 0.0002726281 0.0004332756 0.0003875969 0.0003176620
## 1898 1902 1924 1933 1984 1994
## 0.0003941663 0.0003933910 0.0002986858 0.0003838772 0.0003838772 0.0003838772
## 2001 2011 2019 2026 2042 2063
## 0.0002986858 0.0003176620 0.0002913753 0.0003875969 0.0005136107 0.0002642706
## 2084 2129 2143 2149 2188 2192
## 0.0003875969 0.0003875969 0.0003875969 0.0003875969 0.0003929273 0.0002986858
## 2193 2199 2227 2250 2286 2293
## 0.0003875969 0.0003472222 0.0003386387 0.0002642706 0.0003838772 0.0003933910
## 2316 2329 2330 2335 2349 2350
## 0.0004020909 0.0004020909 0.0002986858 0.0003176620 0.0003714710 0.0003875969
## 2377 2380 2388 2393 2405 2415
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## 2426 2427 2460 2462 2466 2471
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## 2484 2491 2492 2502 2504 2516
## 0.0003838772 0.0003714710 0.0003838772 0.0003838772 0.0003933910 0.0002913753
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## 0.0003176620 0.0002930832 0.0003875969 0.0002478315 0.0003875969 0.0003838772
## 2557 2562 2582 2596 2601 2603
## 0.0002642706 0.0003929273 0.0002843332 0.0004016064 0.0003838772 0.0003933910
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## 0.0002924832 0.0004020909 0.0003787879 0.0002924832 0.0002913753 0.0002986858
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## 0.0002986858 0.0003875969 0.0002977077 0.0003875969 0.0002168727 0.0003875969
## 2844 2846 2849 2861 2872 2876
## 0.0003875969 0.0004020909 0.0003875969 0.0003601008 0.0002767783 0.0003933910
## 2908 2920 2951 2961 2986 2987
## 0.0003838772 0.0003838772 0.0003612717 0.0003176620 0.0003933910 0.0002924832
## 3009 3020 3029 3051 3058 3065
## 0.0004019293 0.0003714710 0.0003875969 0.0003838772 0.0002986858 0.0003875969
## 3066 3091 3103 3119 3134 3136
## 0.0003838772 0.0003875969 0.0002924832 0.0003875969 0.0003639010 0.0002913753
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## 0.0003933910 0.0003838772 0.0003243594 0.0003933910 0.0003466205 0.0003838772
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## 0.0003933910 0.0003714710 0.0003838772 0.0003900156 0.0003575259 0.0002986858
## 3873 3893 3940 3965 3987 3989
## 0.0003767898 0.0003838772 0.0003176620 0.0003612717 0.0003472222 0.0002987750
## 4010 4011 4014 4068 4070 4080
## 0.0004011231 0.0002924832 0.0003900156 0.0003780718 0.0003900156 0.0002853067
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## 0.0003900156 0.0003838772 0.0002642706 0.0003838772 0.0003612717 0.0003900156
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## 0.0003900156 0.0003612717 0.0003900156 0.0002987750 0.0003838772 0.0003933910
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## 0.0003877472 0.0003900156 0.0003838772 0.0002987750 0.0002843332 0.0003557453
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## 0.0003838772 0.0003612717 0.0003900156 0.0003900156 0.0002924832 0.0003838772
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## 0.0004011231 0.0003838772 0.0003466205 0.0003466205 0.0003900156 0.0003933910
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## 0.0004175365 0.0002913753 0.0003933910 0.0003838772 0.0002843332 0.0003838772
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## 0.0003927730 0.0003838772 0.0002924832 0.0003900156 0.0003466205 0.0002642706
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## 0.0002987750 0.0003472222 0.0003900156 0.0003933910 0.0003580380 0.0003472222
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## 0.0003466205 0.0003472222 0.0003557453 0.0003838772 0.0002924832 0.0004011231
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## 0.0002930832 0.0003900156 0.0003900156 0.0002642706 0.0003937008 0.0004060089
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## 0.0002853067 0.0003049710 0.0003176620 0.0003838772 0.0003176620 0.0003900156
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## 0.0003838772 0.0003612717 0.0003612717 0.0003838772 0.0003838772 0.0002987750
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## 0.0003900156 0.0003472222 0.0003900156 0.0003472222 0.0003900156 0.0003176620
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## 0.0003935458 0.0004175365 0.0003935458 0.0002930832 0.0003929273 0.0003900156
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## 0.0003844675 0.0003408316 0.0002987750 0.0003900156 0.0002913753 0.0002508151
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## 0.0003900156 0.0003466205 0.0003844675 0.0003844675 0.0003844675 0.0003844675
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## 0.0003844675 0.0004011231 0.0003900156 0.0002987750 0.0003900156 0.0003844675
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## 0.0004011231 0.0003900156 0.0003844675 0.0003844675 0.0003567606 0.0003176620
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## 0.0003844675 0.0003844675 0.0003844675 0.0003567606 0.0003900156 0.0003935458
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## 0.0002987750 0.0003844675 0.0004011231 0.0002922268 0.0003176620 0.0003866976
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## 0.0003900156 0.0003900156 0.0003844675 0.0004363002 0.0002912056 0.0003844675
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## 0.0003920031 0.0003943218 0.0003472222 0.0003943218 0.0002911208 0.0003844675
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## 0.0003900156 0.0003900156 0.0003935458 0.0002913753 0.0003492840 0.0003900156
## 6169 6173 6190 6202 6221 6223
## 0.0002913753 0.0003567606 0.0003844675 0.0002853067 0.0002987750 0.0003900156
## 6258 6322 6336 6345 6349 6350
## 0.0003900156 0.0002987750 0.0003176620 0.0003552398 0.0003472222 0.0002913753
## 6354 6367 6370 6373 6391 6404
## 0.0002987750 0.0003844675 0.0003844675 0.0003567606 0.0003844675 0.0002987750
## 6451 6472 6498 6505 6554 6563
## 0.0003900156 0.0003844675 0.0003844675 0.0003900156 0.0003900156 0.0002987750
## 6576 6591 6600 6630 6647 6679
## 0.0003935458 0.0003866976 0.0003612717 0.0004330879 0.0003552398 0.0003844675
## 6690 6695 6742 6757 6764 6784
## 0.0002913753 0.0003844675 0.0003900156 0.0003900156 0.0002987750 0.0003866976
## 6785 6815 6821 6857 6860 6869
## 0.0003844675 0.0004277160 0.0003900156 0.0003472222 0.0003900156 0.0004008016
## 6876 6880 6901 6914 6918 6920
## 0.0003552398 0.0003930818 0.0002987750 0.0002773156 0.0002416043 0.0003901678
## 6938 6939 6943 6946 6949 6955
## 0.0003844675 0.0003539823 0.0003844675 0.0003844675 0.0003901678 0.0003933910
## 6962 7026 7041 7048 7082 7103
## 0.0003866976 0.0003901678 0.0002987750 0.0003901678 0.0003901678 0.0003901678
## 7109 7116 7129 7137 7194 7202
## 0.0002913753 0.0002912056 0.0003901678 0.0003176620 0.0003844675 0.0003901678
## 7242 7261 7277 7295 7313 7316
## 0.0003901678 0.0003844675 0.0003552398 0.0003567606 0.0002987750 0.0003844675
## 7335 7337 7345 7352 7367 7373
## 0.0003844675 0.0003639010 0.0002912056 0.0003844675 0.0003901678 0.0003938558
## 7375 7392 7397 7398 7401 7430
## 0.0003901678 0.0003901678 0.0002912056 0.0003844675 0.0002987750 0.0003612717
## 7456 7458 7461 7472 7480 7482
## 0.0002987750 0.0003472222 0.0003933910 0.0002642706 0.0003901678 0.0003844675
## 7483 7508 7514 7564 7566 7569
## 0.0003941663 0.0003844675 0.0003901678 0.0003557453 0.0003933910 0.0003607504
## 7578 7619 7623 7624 7649 7697
## 0.0003844675 0.0003901678 0.0003901678 0.0003946330 0.0003176620 0.0002987750
## 7718 7731 7746 7756 7762 7765
## 0.0003844675 0.0003844675 0.0003472222 0.0003938558 0.0002726281 0.0003933910
## 7772 7779 7825 7830 7837 7850
## 0.0003897116 0.0003901678 0.0003492840 0.0002912056 0.0003933910 0.0003176620
## 7859 7862 7887 7890 7902 7904
## 0.0003472222 0.0003492840 0.0003844675 0.0003176620 0.0003714710 0.0003901678
## 7905 7941 7962 7964 7966 7983
## 0.0003844675 0.0003844675 0.0003612717 0.0002987750 0.0003933910 0.0003844675
## 8006 8015 8030 8077 8113 8123
## 0.0002987750 0.0003844675 0.0003492840 0.0003844675 0.0003844675 0.0004011231
## 8133 8134 8168 8174 8181 8183
## 0.0003933910 0.0004690432 0.0003472222 0.0003714710 0.0002913753 0.0003844675
## 8208 8220 8223 8253 8267 8293
## 0.0003844675 0.0003901678 0.0003901678 0.0003472222 0.0002942908 0.0003844675
## 8294 8309 8338 8342 8362 8365
## 0.0003901678 0.0003846154 0.0003933910 0.0003844675 0.0002912056 0.0002913753
## 8370 8373 8382 8393 8413 8426
## 0.0002911208 0.0003933910 0.0003844675 0.0003581662 0.0002991325 0.0003933910
## 8485 8542 8546 8566 8593 8609
## 0.0003901678 0.0003844675 0.0003901678 0.0003933910 0.0003901678 0.0003938558
## 8627 8663 8679 8708 8742 8746
## 0.0002853067 0.0003612717 0.0002913753 0.0003492840 0.0002991325 0.0003714710
## 8751 8761 8783 8787 8798 8825
## 0.0003408316 0.0003844675 0.0003581662 0.0003581662 0.0003844675 0.0003933910
## 8831 8850 8868 8878 8880 8894
## 0.0003844675 0.0002991325 0.0003901678 0.0003215434 0.0003929273 0.0003935458
## 8914 8931 8939 8953 8957 8992
## 0.0003472222 0.0003844675 0.0003472222 0.0002991325 0.0003581662 0.0004011231
## 8997 9015 9018 9025 9048 9061
## 0.0003935458 0.0003901678 0.0002168727 0.0003901678 0.0003714710 0.0003581662
## 9095 9098 9117 9129 9140 9163
## 0.0003844675 0.0004330879 0.0003844675 0.0003985652 0.0003941663 0.0002912056
## 9204 9210 9228 9229 9250 9253
## 0.0003901678 0.0003901678 0.0003844675 0.0003844675 0.0003929273 0.0003935458
## 9309 9310 9320 9341 9350 9355
## 0.0003612717 0.0003901678 0.0003567606 0.0003901678 0.0003935458 0.0003901678
## 9358 9373 9384 9407 9413 9424
## 0.0002936858 0.0002991325 0.0003612717 0.0003176620 0.0003472222 0.0003844675
## 9481 9486 9500 9553 9568 9570
## 0.0003581662 0.0002991325 0.0002991325 0.0003714710 0.0003844675 0.0003176620
## 9596 9607 9619 9641 9652 9659
## 0.0002913753 0.0002463054 0.0003844675 0.0003176620 0.0003714710 0.0004033885
## 9661 9664 9679 9682 9684 9714
## 0.0003901678 0.0002923977 0.0002911208 0.0003901678 0.0003844675 0.0002633658
## 9726 9763 9766 9782 9785 9792
## 0.0003201024 0.0003201024 0.0002913753 0.0003481894 0.0003901678 0.0003173596
## 9794 9809 9816 9834 9848 9851
## 0.0003901678 0.0002913753 0.0003844675 0.0003844675 0.0004703669 0.0002923977
## 9857 9877 9895 9905 9920 10003
## 0.0002840102 0.0003901678 0.0003481894 0.0003844675 0.0002913753 0.0003844675
## 10012 10054 10105 10110 10117 10118
## 0.0003844675 0.0003891051 0.0003898635 0.0003492840 0.0002991325 0.0003481894
## 10130 10155 10178 10201 10206 10209
## 0.0003901678 0.0003901678 0.0003929273 0.0003901678 0.0003944773 0.0003901678
## 10212 10241 10262 10270 10279 10290
## 0.0002845760 0.0003985652 0.0002922268 0.0003901678 0.0003472222 0.0003844675
## 10303 10364 10371 10386 10404 10423
## 0.0003844675 0.0003866976 0.0003901678 0.0003955696 0.0003933910 0.0003173596
## 10439 10449 10475 10480 10487 10500
## 0.0003844675 0.0003935458 0.0002805836 0.0003173596 0.0003844675 0.0003901678
## 10512 10516 10527 10544 10549 10568
## 0.0003581662 0.0003612717 0.0003901678 0.0003935458 0.0003844675 0.0003935458
## 10572 10582 10590 10638 10650 10694
## 0.0003844675 0.0003173596 0.0003935458 0.0004315926 0.0003392130 0.0003612717
## 10708 10724 10730 10736 10759 10771
## 0.0003639010 0.0003472222 0.0003901678 0.0003844675 0.0003901678 0.0003581662
## 10774 10776 10782 10802 10808 10836
## 0.0003901678 0.0002923977 0.0002991325 0.0003901678 0.0003844675 0.0003601008
## 10862 10874 10882 10886 10891 10906
## 0.0003901678 0.0003714710 0.0003935458 0.0002633658 0.0003243594 0.0003901678
## 10920 10929 10944 10952 10974 10995
## 0.0003935458 0.0003901678 0.0003901678 0.0002922268 0.0004011231 0.0003844675
## 11011 11025 11044 11073 11074 11123
## 0.0002922268 0.0003844675 0.0003581662 0.0004175365 0.0003935458 0.0002923977
## 11132 11142 11146 11156 11164 11171
## 0.0002991325 0.0002923977 0.0003901678 0.0003557453 0.0002794857 0.0003557453
## 11178 11217 11225 11234 11257 11296
## 0.0003176620 0.0003844675 0.0003901678 0.0004293688 0.0003901678 0.0003844675
## 11302 11305 11342 11352 11388 11390
## 0.0003639010 0.0003176620 0.0003276540 0.0003472222 0.0003492840 0.0003901678
## 11396 11417 11443 11456 11457 11461
## 0.0002633658 0.0003844675 0.0003612717 0.0003552398 0.0002845760 0.0002416043
## 11504 11527 11559 11560 11592 11606
## 0.0002924832 0.0002991325 0.0003935458 0.0003844675 0.0003714710 0.0003901678
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## 0.0004636069 0.0003901678 0.0002991325 0.0003844675 0.0003176620 0.0003901678
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## 0.0003901678 0.0004332756 0.0002943774 0.0003567606 0.0004011231 0.0002925688
## 11757 11818 11820 11839 11843 11859
## 0.0003901678 0.0002991325 0.0003176620 0.0002991325 0.0003492840 0.0003937008
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## 0.0002991325 0.0003390980 0.0003844675 0.0003901678 0.0003639010 0.0003901678
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## 0.0003937008 0.0003901678 0.0003844675 0.0003937008 0.0003474635 0.0003844675
## 12084 12091 12109 12114 12117 12128
## 0.0003844675 0.0003901678 0.0003944773 0.0002991325 0.0003472222 0.0002633658
## 12147 12160 12166 12190 12216 12236
## 0.0004011231 0.0002991325 0.0003601008 0.0003844675 0.0004011231 0.0003937008
## 12283 12307 12335 12367 12372 12376
## 0.0003714710 0.0003474635 0.0004011231 0.0003844675 0.0002633658 0.0003844675
## 12380 12382 12389 12395 12397 12408
## 0.0003937008 0.0003472222 0.0003714710 0.0003714710 0.0003937008 0.0003901678
## 12427 12439 12450 12452 12465 12468
## 0.0003844675 0.0003552398 0.0002991325 0.0002991325 0.0003901678 0.0003176620
## 12490 12496 12522 12527 12528 12537
## 0.0003937008 0.0003937008 0.0002924832 0.0002927400 0.0003901678 0.0003901678
## 12546 12550 12552 12554 12561 12568
## 0.0002924832 0.0003844675 0.0003844675 0.0003639010 0.0003943218 0.0003844675
## 12570 12579 12593 12607 12609 12622
## 0.0003901678 0.0003901678 0.0003612717 0.0003901678 0.0003844675 0.0003472222
## 12648 12705 12707 12739 12751 12761
## 0.0003844675 0.0002991325 0.0004011231 0.0003567606 0.0002991325 0.0004347826
## 12782 12799 12804 12806 12816 12843
## 0.0002991325 0.0003901678 0.0002924832 0.0003901678 0.0002853067 0.0003472222
## 12887 12888 12894 12901 12906 12918
## 0.0003901678 0.0003901678 0.0003844675 0.0003926188 0.0003901678 0.0003472222
## 12933 12943 12948 12956 12964 12968
## 0.0003206156 0.0002927400 0.0003220612 0.0003927730 0.0002991325 0.0003901678
## 12980 12984 13000 13035 13052 13087
## 0.0003844675 0.0003901678 0.0004011231 0.0003901678 0.0002991325 0.0003930818
## 13092 13104 13111 13133 13136 13137
## 0.0003844675 0.0003844675 0.0003937008 0.0003844675 0.0003895598 0.0002991325
## 13138 13143 13150 13154 13159 13188
## 0.0003639010 0.0003901678 0.0003581662 0.0003175611 0.0003844675 0.0003901678
## 13252 13261 13263 13284 13335 13336
## 0.0003901678 0.0003844675 0.0004175365 0.0003175611 0.0003175611 0.0003901678
## 13341 13360 13396 13450 13469 13497
## 0.0003844675 0.0003937008 0.0003901678 0.0003901678 0.0002853067 0.0003175611
## 13540 13553 13565 13586 13602 13621
## 0.0003898635 0.0003844675 0.0003639010 0.0002924832 0.0003937008 0.0003175611
## 13635 13636 13647 13656 13660 13674
## 0.0002845760 0.0003581662 0.0003901678 0.0003175611 0.0003472222 0.0003901678
## 13675 13682 13690 13732 13737 13742
## 0.0003901678 0.0002991325 0.0003175611 0.0003901678 0.0003474635 0.0003937008
## 13753 13755 13761 13777 13778 13782
## 0.0003901678 0.0003844675 0.0003844675 0.0003996803 0.0003639010 0.0003937008
## 13784 13791 13807 13892 13916 13962
## 0.0003844675 0.0002991325 0.0003901678 0.0003901678 0.0002770083 0.0003901678
## 13967 13989 14009 14035 14037 14060
## 0.0003844675 0.0003844675 0.0002927400 0.0003901678 0.0003844675 0.0003844675
## 14061 14064 14085 14095 14104 14110
## 0.0003714710 0.0003552398 0.0003901678 0.0003937008 0.0002633658 0.0002912056
## 14118 14119 14122 14125 14135 14137
## 0.0003175611 0.0003384095 0.0003901678 0.0003901678 0.0003901678 0.0003844675
## 14158 14187 14203 14204 14210 14216
## 0.0002912056 0.0003844675 0.0003937008 0.0003185728 0.0003901678 0.0003474635
## 14221 14256 14305 14311 14318 14321
## 0.0002912056 0.0003601008 0.0003901678 0.0003683241 0.0003901678 0.0003844675
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## 0.0004791567 0.0003937008 0.0003844675 0.0003844675 0.0002991325 0.0003844675
## 14404 14414 14422 14435 14443 14481
## 0.0005350455 0.0003844675 0.0003903201 0.0003903201 0.0003472222 0.0002991325
## 14543 14571 14581 14592 14609 14628
## 0.0003903201 0.0003462604 0.0003713331 0.0003903201 0.0002507523 0.0003903201
## 14633 14635 14664 14683 14692 14725
## 0.0003472222 0.0003937008 0.0003903201 0.0002633658 0.0003937008 0.0003844675
## 14741 14743 14747 14751 14762 14780
## 0.0003844675 0.0003903201 0.0003844675 0.0003903201 0.0004017678 0.0003844675
## 14781 14787 14817 14819 14860 14895
## 0.0002876043 0.0003903201 0.0002991325 0.0003844675 0.0002633658 0.0002912056
## 14907 14925 14945 14947 14955 14963
## 0.0003903201 0.0003844675 0.0003903201 0.0003612717 0.0003175611 0.0003244646
## 15005 15024 15037 15096 15134 15143
## 0.0003963535 0.0003844675 0.0003844675 0.0003903201 0.0002912056 0.0003904725
## 15194 15196 15200 15217 15220 15242
## 0.0003937008 0.0003472222 0.0002912056 0.0002910361 0.0003182686 0.0003471017
## 15249
## 0.0003903201hist(closeness_centrality, labels = TRUE, ylim = c(0,700), xlab = "Closeness Centrality", main = "Histograma de los Valores de Closeness Centrality")
Graficamos a continuación la red de usuarios - usuarios donde cada nodo tiene el tamaño y color asociado a la métrica de closeness calculada anteriormente.
colorfunc <- colorRampPalette(c( "red",
"firebrick",
"lightcoral",
"saddlebrown",
"tomato",
"darkorange",
"gold",
"lightgreen",
"mediumspringgreen",
"dodgerblue",
"steelblue"))
scaled_closeness = closeness_centrality * 10000
colors <- colorfunc(max(scaled_closeness))
V(users_projected)$color <- colors[scaled_closeness]
V(users_projected)$shape <- "circle"
E(users_projected)$color <- "lightgray"
plot(users_projected,
vertex.label = format(round(closeness_centrality, 2), nsmall = 1),
vertex.size = closeness_centrality *1000 / 2,
edge.arrow.size=0.1,
layout = layout_components,
main = "Proyección Red Usuarios: Nodos según Closeness Centrality")
plot(users_projected,
vertex.label = NA,
vertex.size = closeness_centrality *1000 / 2,
edge.arrow.size=0.1,
layout = layout_components,
main = "Proyección Red Usuarios: Nodos según Closeness Centrality")
Betweenness Centrality
betweenness_centrality <- betweenness(users_projected)
betweenness_centrality## 10 11 41 51 114 67
## 2.509666e+01 0.000000e+00 1.134090e+01 4.617509e+00 9.582294e-01 3.514690e+00
## 78 82 85 98 107 128
## 1.111482e+00 5.322964e+02 1.920345e+02 3.514690e+00 2.509666e+01 0.000000e+00
## 132 133 134 143 144 169
## 3.359815e-01 1.313748e-01 5.684126e+02 2.735390e-01 1.134090e+01 2.509666e+01
## 182 187 189 203 231 234
## 2.962600e+00 5.416662e-01 3.514690e+00 4.617509e+00 4.617509e+00 2.509666e+01
## 246 254 257 258 281 306
## 1.111482e+00 5.841984e+03 0.000000e+00 2.481715e+03 3.514690e+00 5.416662e-01
## 345 349 354 376 393 398
## 0.000000e+00 0.000000e+00 3.514690e+00 1.104132e+01 1.033604e+00 0.000000e+00
## 448 456 466 470 471 478
## 3.514690e+00 3.359815e-01 1.676430e+03 3.514690e+00 9.637984e-01 4.617509e+00
## 499 519 521 522 529 533
## 0.000000e+00 1.112814e+01 4.617509e+00 4.748416e+02 4.617509e+00 0.000000e+00
## 568 595 620 639 648 656
## 1.188829e+03 2.905002e-01 3.514690e+00 0.000000e+00 1.104132e+01 2.509666e+01
## 657 679 697 743 771 796
## 1.136210e+02 5.684126e+02 1.111482e+00 2.735390e-01 4.748416e+02 0.000000e+00
## 822 837 838 840 841 859
## 0.000000e+00 2.735390e-01 7.296816e+01 3.514690e+00 3.359815e-01 4.617509e+00
## 861 873 917 940 946 948
## 0.000000e+00 0.000000e+00 3.514690e+00 2.500398e+03 0.000000e+00 9.637984e-01
## 968 973 976 994 1000 1002
## 6.338504e+02 2.509666e+01 5.416662e-01 3.514690e+00 3.514690e+00 2.962600e+00
## 1024 1031 1032 1039 1055 1076
## 2.509666e+01 7.296816e+01 0.000000e+00 2.509666e+01 2.735390e-01 3.514690e+00
## 1102 1110 1138 1163 1172 1175
## 2.509666e+01 2.544933e+02 2.509666e+01 4.622072e+03 2.509666e+01 5.684126e+02
## 1209 1214 1218 1223 1224 1257
## 4.617509e+00 2.509666e+01 3.514690e+00 2.509666e+01 3.514690e+00 2.509666e+01
## 1258 1280 1307 1313 1329 1362
## 5.416662e-01 2.509666e+01 5.992822e+02 2.962600e+00 2.509666e+01 9.582294e-01
## 1412 1415 1416 1424 1429 1445
## 3.514690e+00 4.617509e+00 3.514690e+00 4.617509e+00 1.134090e+01 1.104132e+01
## 1462 1467 1481 1483 1494 1507
## 1.134090e+01 5.684126e+02 4.617509e+00 7.296816e+01 2.962600e+00 3.514690e+00
## 1544 1559 1596 1598 1639 1657
## 4.748416e+02 1.646644e+04 3.514690e+00 1.134090e+01 1.111482e+00 1.313748e-01
## 1660 1705 1709 1718 1739 1752
## 3.514690e+00 2.509666e+01 4.617509e+00 9.249953e+02 1.111482e+00 4.617509e+00
## 1760 1790 1826 1833 1849 1884
## 1.111482e+00 0.000000e+00 0.000000e+00 3.933553e+03 3.514690e+00 1.111482e+00
## 1898 1902 1924 1933 1984 1994
## 4.748416e+02 4.617509e+00 0.000000e+00 2.509666e+01 2.509666e+01 2.509666e+01
## 2001 2011 2019 2026 2042 2063
## 0.000000e+00 1.111482e+00 5.416662e-01 3.514690e+00 2.314038e+05 0.000000e+00
## 2084 2129 2143 2149 2188 2192
## 3.514690e+00 3.514690e+00 3.514690e+00 3.514690e+00 1.595494e+02 0.000000e+00
## 2193 2199 2227 2250 2286 2293
## 3.514690e+00 7.296816e+01 1.188829e+03 0.000000e+00 2.509666e+01 4.617509e+00
## 2316 2329 2330 2335 2349 2350
## 5.684126e+02 5.684126e+02 0.000000e+00 1.111482e+00 2.962600e+00 3.514690e+00
## 2377 2380 2388 2393 2405 2415
## 1.104132e+01 3.514690e+00 2.509666e+01 1.111482e+00 2.735390e-01 4.617509e+00
## 2426 2427 2460 2462 2466 2471
## 4.564174e+03 8.338104e+03 7.296816e+01 3.514690e+00 9.637984e-01 3.514690e+00
## 2484 2491 2492 2502 2504 2516
## 2.509666e+01 2.962600e+00 2.509666e+01 2.509666e+01 4.617509e+00 5.416662e-01
## 2524 2526 2527 2533 2542 2549
## 1.111482e+00 2.180673e+02 3.514690e+00 0.000000e+00 3.514690e+00 2.509666e+01
## 2557 2562 2582 2596 2601 2603
## 0.000000e+00 1.595494e+02 3.359815e-01 1.649925e+03 2.509666e+01 4.617509e+00
## 2623 2660 2663 2701 2720 2730
## 1.033604e+00 5.684126e+02 1.213503e+03 1.033604e+00 5.416662e-01 0.000000e+00
## 2738 2795 2806 2811 2820 2835
## 0.000000e+00 3.514690e+00 0.000000e+00 3.514690e+00 0.000000e+00 3.514690e+00
## 2844 2846 2849 2861 2872 2876
## 3.514690e+00 5.684126e+02 3.514690e+00 1.313748e-01 0.000000e+00 4.617509e+00
## 2908 2920 2951 2961 2986 2987
## 2.509666e+01 2.509666e+01 1.104132e+01 1.111482e+00 4.617509e+00 1.033604e+00
## 3009 3020 3029 3051 3058 3065
## 5.684126e+02 2.962600e+00 3.514690e+00 2.509666e+01 0.000000e+00 3.514690e+00
## 3066 3091 3103 3119 3134 3136
## 2.509666e+01 3.514690e+00 1.033604e+00 3.514690e+00 2.735390e-01 5.416662e-01
## 3137 3150 3152 3158 3167 3186
## 9.637984e-01 3.514690e+00 4.970885e+03 8.370871e+02 1.033604e+00 3.514690e+00
## 3187 3200 3230 3260 3265 3278
## 4.214785e+02 5.306754e+03 2.509666e+01 0.000000e+00 1.357050e+03 0.000000e+00
## 3298 3314 3327 3345 3350 3356
## 3.514690e+00 3.514690e+00 3.514690e+00 3.514690e+00 2.509666e+01 7.251781e+01
## 3384 3397 3401 3404 3408 3411
## 3.514690e+00 1.097181e+04 5.416662e-01 1.111482e+00 3.514690e+00 1.111482e+00
## 3443 3459 3461 3469 3491 3505
## 3.514690e+00 3.514690e+00 2.905002e-01 4.617509e+00 7.296816e+01 0.000000e+00
## 3516 3519 3533 3549 3554 3557
## 4.822009e+03 1.188829e+03 2.509666e+01 5.416662e-01 4.617509e+00 3.514690e+00
## 3571 3577 3595 3596 3635 3648
## 2.509666e+01 1.111482e+00 1.111482e+00 4.617509e+00 2.962600e+00 3.514690e+00
## 3651 3723 3727 3728 3733 3750
## 4.617509e+00 2.509666e+01 5.552928e+02 4.617509e+00 7.251781e+01 2.509666e+01
## 3752 3753 3790 3833 3845 3857
## 4.617509e+00 2.962600e+00 2.509666e+01 3.514690e+00 1.112814e+01 0.000000e+00
## 3873 3893 3940 3965 3987 3989
## 7.358650e+03 2.509666e+01 1.111482e+00 1.104132e+01 7.296816e+01 0.000000e+00
## 4010 4011 4014 4068 4070 4080
## 5.684126e+02 1.033604e+00 3.514690e+00 6.338504e+02 3.514690e+00 2.905002e-01
## 4081 4102 4124 4159 4214 4229
## 3.514690e+00 2.509666e+01 0.000000e+00 2.509666e+01 1.104132e+01 3.514690e+00
## 4243 4245 4273 4276 4303 4304
## 3.514690e+00 1.104132e+01 3.514690e+00 0.000000e+00 2.509666e+01 4.617509e+00
## 4310 4323 4350 4352 4369 4374
## 2.509666e+01 3.514690e+00 0.000000e+00 0.000000e+00 3.514690e+00 7.251781e+01
## 4384 4432 4461 4470 4472 4476
## 2.596233e+03 3.514690e+00 0.000000e+00 0.000000e+00 2.509666e+01 2.509666e+01
## 4489 4526 4536 4542 4550 4590
## 2.481715e+03 3.514690e+00 2.509666e+01 0.000000e+00 3.359815e-01 2.502509e+02
## 4600 4615 4633 4667 4705 4708
## 2.509666e+01 1.104132e+01 3.514690e+00 3.514690e+00 1.033604e+00 2.509666e+01
## 4730 4750 4751 4756 4760 4792
## 5.684126e+02 2.509666e+01 7.251781e+01 7.251781e+01 3.514690e+00 4.617509e+00
## 4800 4801 4820 4822 4823 4833
## 1.966330e-01 5.416662e-01 4.617509e+00 2.509666e+01 3.359815e-01 2.509666e+01
## 4843 4846 4862 4864 4875 4879
## 2.544933e+02 2.509666e+01 1.033604e+00 3.514690e+00 7.251781e+01 0.000000e+00
## 4890 4904 4910 4921 4930 4954
## 0.000000e+00 7.296816e+01 3.514690e+00 4.617509e+00 1.233139e+02 7.296816e+01
## 4964 4982 5014 5015 5044 5049
## 7.251781e+01 7.296816e+01 1.134090e+01 2.509666e+01 1.033604e+00 5.684126e+02
## 5055 5057 5072 5103 5115 5120
## 2.180673e+02 3.514690e+00 3.514690e+00 0.000000e+00 4.748416e+02 4.146368e+04
## 5132 5136 5146 5179 5189 5223
## 2.905002e-01 9.358170e+01 1.111482e+00 2.509666e+01 1.111482e+00 3.514690e+00
## 5231 5251 5292 5293 5327 5331
## 2.509666e+01 1.104132e+01 1.104132e+01 2.509666e+01 2.509666e+01 0.000000e+00
## 5342 5359 5423 5424 5438 5446
## 3.514690e+00 7.296816e+01 3.514690e+00 7.296816e+01 3.514690e+00 1.111482e+00
## 5453 5461 5466 5507 5508 5531
## 4.617509e+00 1.966330e-01 7.861847e+03 2.180673e+02 7.870801e+02 3.514690e+00
## 5547 5552 5554 5556 5564 5579
## 2.509666e+01 1.676430e+03 0.000000e+00 3.514690e+00 5.416662e-01 0.000000e+00
## 5583 5597 5624 5666 5670 5688
## 3.514690e+00 7.251781e+01 2.509666e+01 2.509666e+01 2.509666e+01 2.509666e+01
## 5707 5714 5726 5731 5771 5793
## 2.509666e+01 5.684126e+02 3.514690e+00 0.000000e+00 3.514690e+00 2.509666e+01
## 5846 5849 5867 5881 5886 5899
## 5.684126e+02 3.514690e+00 2.509666e+01 2.509666e+01 9.637984e-01 1.111482e+00
## 5901 5902 5904 5905 5906 5911
## 2.509666e+01 2.509666e+01 2.509666e+01 9.637984e-01 3.514690e+00 4.617509e+00
## 5912 5918 5920 5939 5948 5962
## 0.000000e+00 2.509666e+01 5.684126e+02 1.033604e+00 1.111482e+00 2.481715e+03
## 5965 5973 5994 5996 5999 6003
## 3.514690e+00 3.514690e+00 2.509666e+01 9.351431e+03 1.033604e+00 2.509666e+01
## 6009 6018 6022 6054 6078 6085
## 2.544933e+02 2.596233e+03 7.296816e+01 2.008817e+03 1.582054e+02 2.509666e+01
## 6118 6121 6125 6134 6145 6163
## 3.514690e+00 3.514690e+00 4.617509e+00 5.416662e-01 7.251781e+01 3.514690e+00
## 6169 6173 6190 6202 6221 6223
## 5.416662e-01 1.112814e+01 2.509666e+01 2.905002e-01 0.000000e+00 3.514690e+00
## 6258 6322 6336 6345 6349 6350
## 3.514690e+00 0.000000e+00 1.111482e+00 8.405205e-01 7.296816e+01 5.416662e-01
## 6354 6367 6370 6373 6391 6404
## 0.000000e+00 2.509666e+01 2.509666e+01 9.637984e-01 2.509666e+01 0.000000e+00
## 6451 6472 6498 6505 6554 6563
## 3.514690e+00 2.509666e+01 2.509666e+01 3.514690e+00 3.514690e+00 0.000000e+00
## 6576 6591 6600 6630 6647 6679
## 4.617509e+00 2.481715e+03 1.104132e+01 3.933553e+03 8.405205e-01 2.509666e+01
## 6690 6695 6742 6757 6764 6784
## 5.416662e-01 2.509666e+01 3.514690e+00 3.514690e+00 0.000000e+00 2.481715e+03
## 6785 6815 6821 6857 6860 6869
## 2.509666e+01 4.665614e+03 3.514690e+00 7.296816e+01 3.514690e+00 5.684126e+02
## 6876 6880 6901 6914 6918 6920
## 8.405205e-01 4.622072e+03 0.000000e+00 2.071896e-01 0.000000e+00 3.514690e+00
## 6938 6939 6943 6946 6949 6955
## 2.509666e+01 9.582294e-01 2.509666e+01 2.509666e+01 3.514690e+00 4.617509e+00
## 6962 7026 7041 7048 7082 7103
## 2.481715e+03 3.514690e+00 0.000000e+00 3.514690e+00 3.514690e+00 3.514690e+00
## 7109 7116 7129 7137 7194 7202
## 5.416662e-01 1.033604e+00 3.514690e+00 1.111482e+00 2.509666e+01 3.514690e+00
## 7242 7261 7277 7295 7313 7316
## 3.514690e+00 2.509666e+01 8.405205e-01 9.637984e-01 0.000000e+00 2.509666e+01
## 7335 7337 7345 7352 7367 7373
## 2.509666e+01 2.735390e-01 1.033604e+00 2.509666e+01 3.514690e+00 4.748416e+02
## 7375 7392 7397 7398 7401 7430
## 3.514690e+00 3.514690e+00 1.033604e+00 2.509666e+01 0.000000e+00 1.104132e+01
## 7456 7458 7461 7472 7480 7482
## 0.000000e+00 7.296816e+01 4.617509e+00 0.000000e+00 3.514690e+00 2.509666e+01
## 7483 7508 7514 7564 7566 7569
## 6.829105e+03 2.509666e+01 3.514690e+00 1.134090e+01 4.617509e+00 1.846896e+04
## 7578 7619 7623 7624 7649 7697
## 2.509666e+01 3.514690e+00 3.514690e+00 2.596233e+03 1.111482e+00 0.000000e+00
## 7718 7731 7746 7756 7762 7765
## 2.509666e+01 2.509666e+01 7.296816e+01 4.748416e+02 0.000000e+00 4.617509e+00
## 7772 7779 7825 7830 7837 7850
## 3.218147e+02 3.514690e+00 7.251781e+01 1.033604e+00 4.617509e+00 1.111482e+00
## 7859 7862 7887 7890 7902 7904
## 7.296816e+01 7.251781e+01 2.509666e+01 1.111482e+00 2.962600e+00 3.514690e+00
## 7905 7941 7962 7964 7966 7983
## 2.509666e+01 2.509666e+01 1.104132e+01 0.000000e+00 4.617509e+00 2.509666e+01
## 8006 8015 8030 8077 8113 8123
## 0.000000e+00 2.509666e+01 7.251781e+01 2.509666e+01 2.509666e+01 5.684126e+02
## 8133 8134 8168 8174 8181 8183
## 4.617509e+00 1.637644e+04 7.296816e+01 2.962600e+00 5.416662e-01 2.509666e+01
## 8208 8220 8223 8253 8267 8293
## 2.509666e+01 3.514690e+00 3.514690e+00 7.296816e+01 7.104577e+02 2.509666e+01
## 8294 8309 8338 8342 8362 8365
## 3.514690e+00 2.015970e+04 4.617509e+00 2.509666e+01 1.033604e+00 5.416662e-01
## 8370 8373 8382 8393 8413 8426
## 1.582054e+02 4.617509e+00 2.509666e+01 9.637984e-01 0.000000e+00 4.617509e+00
## 8485 8542 8546 8566 8593 8609
## 3.514690e+00 2.509666e+01 3.514690e+00 4.617509e+00 3.514690e+00 4.748416e+02
## 8627 8663 8679 8708 8742 8746
## 2.905002e-01 1.104132e+01 5.416662e-01 7.251781e+01 0.000000e+00 2.962600e+00
## 8751 8761 8783 8787 8798 8825
## 1.676430e+03 2.509666e+01 9.637984e-01 9.637984e-01 2.509666e+01 4.617509e+00
## 8831 8850 8868 8878 8880 8894
## 2.509666e+01 0.000000e+00 3.514690e+00 5.552928e+02 6.735139e+02 4.617509e+00
## 8914 8931 8939 8953 8957 8992
## 7.296816e+01 2.509666e+01 7.296816e+01 0.000000e+00 9.637984e-01 5.684126e+02
## 8997 9015 9018 9025 9048 9061
## 4.617509e+00 3.514690e+00 0.000000e+00 3.514690e+00 2.962600e+00 9.637984e-01
## 9095 9098 9117 9129 9140 9163
## 2.509666e+01 3.933553e+03 2.509666e+01 9.186297e+03 4.185553e+02 1.033604e+00
## 9204 9210 9228 9229 9250 9253
## 3.514690e+00 3.514690e+00 2.509666e+01 2.509666e+01 6.735139e+02 4.617509e+00
## 9309 9310 9320 9341 9350 9355
## 1.104132e+01 3.514690e+00 1.112814e+01 3.514690e+00 4.617509e+00 3.514690e+00
## 9358 9373 9384 9407 9413 9424
## 2.292348e+02 0.000000e+00 1.104132e+01 1.111482e+00 7.296816e+01 2.509666e+01
## 9481 9486 9500 9553 9568 9570
## 9.637984e-01 0.000000e+00 0.000000e+00 2.962600e+00 2.509666e+01 1.111482e+00
## 9596 9607 9619 9641 9652 9659
## 5.416662e-01 0.000000e+00 2.509666e+01 1.111482e+00 2.962600e+00 1.357050e+03
## 9661 9664 9679 9682 9684 9714
## 3.514690e+00 1.033604e+00 1.582054e+02 3.514690e+00 2.509666e+01 0.000000e+00
## 9726 9763 9766 9782 9785 9792
## 3.230174e+03 3.230174e+03 5.416662e-01 1.613997e-02 3.514690e+00 1.111482e+00
## 9794 9809 9816 9834 9848 9851
## 3.514690e+00 5.416662e-01 2.509666e+01 2.509666e+01 1.637644e+04 1.033604e+00
## 9857 9877 9895 9905 9920 10003
## 1.785714e-02 3.514690e+00 1.613997e-02 2.509666e+01 5.416662e-01 2.509666e+01
## 10012 10054 10105 10110 10117 10118
## 2.509666e+01 3.086590e+03 2.282577e+03 7.251781e+01 0.000000e+00 1.613997e-02
## 10130 10155 10178 10201 10206 10209
## 3.514690e+00 3.514690e+00 2.544933e+02 3.514690e+00 7.132508e+03 3.514690e+00
## 10212 10241 10262 10270 10279 10290
## 3.359815e-01 9.186297e+03 5.416662e-01 3.514690e+00 7.296816e+01 2.509666e+01
## 10303 10364 10371 10386 10404 10423
## 2.509666e+01 2.481715e+03 3.514690e+00 4.680233e+03 4.281273e+02 1.111482e+00
## 10439 10449 10475 10480 10487 10500
## 2.509666e+01 4.617509e+00 1.314655e-01 1.111482e+00 2.509666e+01 3.514690e+00
## 10512 10516 10527 10544 10549 10568
## 9.637984e-01 1.104132e+01 3.514690e+00 4.617509e+00 2.509666e+01 4.617509e+00
## 10572 10582 10590 10638 10650 10694
## 2.509666e+01 1.111482e+00 4.617509e+00 3.898724e+04 7.964028e+02 1.104132e+01
## 10708 10724 10730 10736 10759 10771
## 2.735390e-01 7.296816e+01 3.514690e+00 2.509666e+01 3.514690e+00 9.637984e-01
## 10774 10776 10782 10802 10808 10836
## 3.514690e+00 1.033604e+00 0.000000e+00 3.514690e+00 2.509666e+01 1.313748e-01
## 10862 10874 10882 10886 10891 10906
## 3.514690e+00 2.962600e+00 4.617509e+00 0.000000e+00 5.552928e+02 3.514690e+00
## 10920 10929 10944 10952 10974 10995
## 4.617509e+00 3.514690e+00 3.514690e+00 5.416662e-01 5.684126e+02 2.509666e+01
## 11011 11025 11044 11073 11074 11123
## 5.416662e-01 2.509666e+01 9.637984e-01 1.966330e-01 4.617509e+00 1.033604e+00
## 11132 11142 11146 11156 11164 11171
## 0.000000e+00 1.033604e+00 3.514690e+00 1.134090e+01 0.000000e+00 1.134090e+01
## 11178 11217 11225 11234 11257 11296
## 1.111482e+00 2.509666e+01 3.514690e+00 7.938586e+03 3.514690e+00 2.509666e+01
## 11302 11305 11342 11352 11388 11390
## 2.735390e-01 1.111482e+00 1.476768e+03 7.296816e+01 7.251781e+01 3.514690e+00
## 11396 11417 11443 11456 11457 11461
## 0.000000e+00 2.509666e+01 1.104132e+01 8.405205e-01 3.359815e-01 0.000000e+00
## 11504 11527 11559 11560 11592 11606
## 5.416662e-01 0.000000e+00 4.617509e+00 2.509666e+01 2.962600e+00 3.514690e+00
## 11608 11642 11664 11671 11678 11700
## 3.661025e+04 3.514690e+00 0.000000e+00 2.509666e+01 1.111482e+00 3.514690e+00
## 11704 11707 11728 11729 11735 11753
## 3.514690e+00 3.933553e+03 2.180673e+02 1.112814e+01 5.684126e+02 1.033604e+00
## 11757 11818 11820 11839 11843 11859
## 3.514690e+00 0.000000e+00 1.111482e+00 0.000000e+00 7.251781e+01 4.617509e+00
## 11862 11887 11896 11935 11980 11988
## 0.000000e+00 3.272829e+03 2.509666e+01 3.514690e+00 2.735390e-01 3.514690e+00
## 12016 12030 12032 12049 12051 12075
## 4.617509e+00 3.514690e+00 2.509666e+01 4.617509e+00 7.251781e+01 2.509666e+01
## 12084 12091 12109 12114 12117 12128
## 2.509666e+01 3.514690e+00 8.840404e+03 0.000000e+00 7.296816e+01 0.000000e+00
## 12147 12160 12166 12190 12216 12236
## 5.684126e+02 0.000000e+00 1.313748e-01 2.509666e+01 5.684126e+02 4.617509e+00
## 12283 12307 12335 12367 12372 12376
## 2.962600e+00 7.251781e+01 5.684126e+02 2.509666e+01 0.000000e+00 2.509666e+01
## 12380 12382 12389 12395 12397 12408
## 4.617509e+00 7.296816e+01 2.962600e+00 2.962600e+00 4.617509e+00 3.514690e+00
## 12427 12439 12450 12452 12465 12468
## 2.509666e+01 8.405205e-01 0.000000e+00 0.000000e+00 3.514690e+00 1.111482e+00
## 12490 12496 12522 12527 12528 12537
## 4.617509e+00 4.617509e+00 5.416662e-01 1.033604e+00 3.514690e+00 3.514690e+00
## 12546 12550 12552 12554 12561 12568
## 5.416662e-01 2.509666e+01 2.509666e+01 2.735390e-01 4.281273e+02 2.509666e+01
## 12570 12579 12593 12607 12609 12622
## 3.514690e+00 3.514690e+00 1.104132e+01 3.514690e+00 2.509666e+01 7.296816e+01
## 12648 12705 12707 12739 12751 12761
## 2.509666e+01 0.000000e+00 5.684126e+02 1.112814e+01 0.000000e+00 4.348904e+03
## 12782 12799 12804 12806 12816 12843
## 0.000000e+00 3.514690e+00 5.416662e-01 3.514690e+00 2.905002e-01 7.296816e+01
## 12887 12888 12894 12901 12906 12918
## 3.514690e+00 3.514690e+00 2.509666e+01 4.622072e+03 3.514690e+00 7.296816e+01
## 12933 12943 12948 12956 12964 12968
## 1.684093e+03 1.033604e+00 3.230174e+03 2.544933e+02 0.000000e+00 3.514690e+00
## 12980 12984 13000 13035 13052 13087
## 2.509666e+01 3.514690e+00 5.684126e+02 3.514690e+00 0.000000e+00 4.748416e+02
## 13092 13104 13111 13133 13136 13137
## 2.509666e+01 2.509666e+01 4.617509e+00 2.509666e+01 3.218147e+02 0.000000e+00
## 13138 13143 13150 13154 13159 13188
## 2.735390e-01 3.514690e+00 9.637984e-01 1.111482e+00 2.509666e+01 3.514690e+00
## 13252 13261 13263 13284 13335 13336
## 3.514690e+00 2.509666e+01 1.966330e-01 1.111482e+00 1.111482e+00 3.514690e+00
## 13341 13360 13396 13450 13469 13497
## 2.509666e+01 4.617509e+00 3.514690e+00 3.514690e+00 2.905002e-01 1.111482e+00
## 13540 13553 13565 13586 13602 13621
## 2.282577e+03 2.509666e+01 2.735390e-01 5.416662e-01 4.617509e+00 1.111482e+00
## 13635 13636 13647 13656 13660 13674
## 3.359815e-01 9.637984e-01 3.514690e+00 1.111482e+00 7.296816e+01 3.514690e+00
## 13675 13682 13690 13732 13737 13742
## 3.514690e+00 0.000000e+00 1.111482e+00 3.514690e+00 7.251781e+01 4.617509e+00
## 13753 13755 13761 13777 13778 13782
## 3.514690e+00 2.509666e+01 2.509666e+01 8.146378e+03 2.735390e-01 4.617509e+00
## 13784 13791 13807 13892 13916 13962
## 2.509666e+01 0.000000e+00 3.514690e+00 3.514690e+00 2.071896e-01 3.514690e+00
## 13967 13989 14009 14035 14037 14060
## 2.509666e+01 2.509666e+01 1.033604e+00 3.514690e+00 2.509666e+01 2.509666e+01
## 14061 14064 14085 14095 14104 14110
## 2.962600e+00 8.405205e-01 3.514690e+00 4.617509e+00 0.000000e+00 5.416662e-01
## 14118 14119 14122 14125 14135 14137
## 1.111482e+00 1.188829e+03 3.514690e+00 3.514690e+00 3.514690e+00 2.509666e+01
## 14158 14187 14203 14204 14210 14216
## 5.416662e-01 2.509666e+01 4.617509e+00 1.044062e+02 3.514690e+00 7.251781e+01
## 14221 14256 14305 14311 14318 14321
## 5.416662e-01 1.313748e-01 3.514690e+00 2.198806e+03 3.514690e+00 2.509666e+01
## 14346 14358 14362 14368 14374 14403
## 3.540056e+04 4.617509e+00 2.509666e+01 2.509666e+01 0.000000e+00 2.509666e+01
## 14404 14414 14422 14435 14443 14481
## 1.561911e+05 2.509666e+01 3.514690e+00 3.514690e+00 7.296816e+01 0.000000e+00
## 14543 14571 14581 14592 14609 14628
## 3.514690e+00 1.613997e-02 2.962600e+00 3.514690e+00 0.000000e+00 3.514690e+00
## 14633 14635 14664 14683 14692 14725
## 7.296816e+01 4.617509e+00 3.514690e+00 0.000000e+00 4.617509e+00 2.509666e+01
## 14741 14743 14747 14751 14762 14780
## 2.509666e+01 3.514690e+00 2.509666e+01 3.514690e+00 5.684126e+02 2.509666e+01
## 14781 14787 14817 14819 14860 14895
## 1.308141e+02 3.514690e+00 0.000000e+00 2.509666e+01 0.000000e+00 5.416662e-01
## 14907 14925 14945 14947 14955 14963
## 3.514690e+00 2.509666e+01 3.514690e+00 1.104132e+01 1.111482e+00 8.962168e+02
## 15005 15024 15037 15096 15134 15143
## 1.622603e+03 2.509666e+01 2.509666e+01 3.514690e+00 5.416662e-01 3.218147e+02
## 15194 15196 15200 15217 15220 15242
## 4.617509e+00 7.296816e+01 5.416662e-01 1.582054e+02 1.111482e+00 7.251781e+01
## 15249
## 3.514690e+00hist(betweenness_centrality, labels = TRUE, ylim = c(0,2000), xlim = c(0,250000), xlab = "Betweenness Centrality", main = "Histograma de los Valores de Betweenness Centrality")
Graficamos a continuación la red de usuarios-usuarios donde cada nodo tiene el tamaño y color asociado a la métrica de betweenness calculada anteriormente.
colorfunc <- colorRampPalette(c( "red",
"firebrick",
"lightcoral",
"saddlebrown",
"tomato",
"darkorange",
"gold",
"lightgreen",
"mediumspringgreen",
"dodgerblue",
"steelblue"))
scaled_betweenness = (betweenness_centrality/1000) +1
colors <- colorfunc(max(scaled_betweenness))
V(users_projected)$color <- colors[scaled_betweenness]
V(users_projected)$shape <- "circle"
E(users_projected)$color <- "lightgray"
plot(users_projected,
vertex.label = NA,
vertex.size = scaled_betweenness,
edge.arrow.size=0.1,
layout = layout_nicely,
main = "Proyeccion Red Usuarios- Nodos Según el Betweenness")
Medidas Sobre Conjuntos de Nodos
Cliques
Cálculo de la distribución de cliques:
count_max_cliques(users_projected)## [1] 79La red contiene 79 cliques.
¿Cuál es el tamaño de cada clique?
total_cliques = max_cliques(users_projected)
cliques_sizes = unlist(lapply(total_cliques, length))
h = hist(cliques_sizes,
ylim=c(0,80),
breaks = 5,
xlab = "Tamaño de los cliques",
ylab = "Frecuencia",
main = "Distribución del tamaño de los cliques" )
text(h$mids,h$counts,labels=h$counts, adj=c(0.5, -0.5))
Graficamos el clique más grande:
largestCliques <- largest_cliques(users_projected)
largestClique_1 = largestCliques[[1]]
g_induced = induced_subgraph(users_projected, largestClique_1)
V(g_induced)$color <- "lightblue"
plot(g_induced,
vertex.label = NA,
layout=layout_components,
edge.arrow.size=0.05,
vertex.size = 10)
Medidas Globales
Radio
Calculado obteniendo el mínimo valor de eccentricity
eccentricity_metric = eccentricity(users_projected)
min(eccentricity_metric)## [1] 3Diametro
diameter_metric = diameter(users_projected)
diameter_metric## [1] 6Average Path Length
avg_path_length = mean_distance(users_projected, directed=F)
avg_path_length## [1] 2.658437Clustering Promedio
clustering_avg = transitivity(users_projected, type = "average")
clustering_avg## [1] 0.9563615Coeficiente de Reciprocidad
reciprocity(users_projected)## [1] 1Subredes
K-Core
La función coreness nos da una tabla indicando que core pertenece a cada nodo:
coreness(users_projected)## 10 11 41 51 114 67 78 82 85 98 107 128 132
## 220 81 28 126 14 253 67 81 3 253 220 18 12
## 133 134 143 144 169 182 187 189 203 231 234 246 254
## 11 253 21 28 220 62 52 253 126 126 220 67 81
## 257 258 281 306 345 349 354 376 393 398 448 456 466
## 81 220 253 52 81 81 253 44 48 81 253 12 220
## 470 471 478 499 519 521 522 529 533 568 595 620 639
## 253 30 126 81 25 126 126 126 81 220 14 253 81
## 648 656 657 679 697 743 771 796 822 837 838 840 841
## 44 220 44 253 67 21 126 81 81 21 46 253 12
## 859 861 873 917 940 946 948 968 973 976 994 1000 1002
## 126 81 81 253 126 81 30 62 220 52 253 253 62
## 1024 1031 1032 1039 1055 1076 1102 1110 1138 1163 1172 1175 1209
## 220 46 18 220 21 253 220 126 220 220 220 253 126
## 1214 1218 1223 1224 1257 1258 1280 1307 1313 1329 1362 1412 1415
## 220 253 220 253 220 52 220 48 62 220 14 253 126
## 1416 1424 1429 1445 1462 1467 1481 1483 1494 1507 1544 1559 1596
## 253 126 28 44 28 253 126 46 62 253 126 126 253
## 1598 1639 1657 1660 1705 1709 1718 1739 1752 1760 1790 1826 1833
## 28 67 11 253 220 126 14 67 126 67 81 4 253
## 1849 1884 1898 1902 1924 1933 1984 1994 2001 2011 2019 2026 2042
## 253 67 126 126 81 220 220 220 81 67 52 253 220
## 2063 2084 2129 2143 2149 2188 2192 2193 2199 2227 2250 2286 2293
## 18 253 253 253 253 126 81 253 46 220 18 220 126
## 2316 2329 2330 2335 2349 2350 2377 2380 2388 2393 2405 2415 2426
## 253 253 81 67 62 253 44 253 220 67 21 126 253
## 2427 2460 2462 2466 2471 2484 2491 2492 2502 2504 2516 2524 2526
## 253 46 253 30 253 220 62 220 220 126 52 67 52
## 2527 2533 2542 2549 2557 2562 2582 2596 2601 2603 2623 2660 2663
## 253 5 253 220 18 126 12 126 220 126 48 253 62
## 2701 2720 2730 2738 2795 2806 2811 2820 2835 2844 2846 2849 2861
## 48 52 81 81 253 3 253 2 253 253 253 253 11
## 2872 2876 2908 2920 2951 2961 2986 2987 3009 3020 3029 3051 3058
## 3 126 220 220 44 67 126 48 253 62 253 220 81
## 3065 3066 3091 3103 3119 3134 3136 3137 3150 3152 3158 3167 3186
## 253 220 253 48 253 21 52 30 253 67 126 48 253
## 3187 3200 3230 3260 3265 3278 3298 3314 3327 3345 3350 3356 3384
## 126 253 220 2 253 81 253 253 253 253 220 31 253
## 3397 3401 3404 3408 3411 3443 3459 3461 3469 3491 3505 3516 3519
## 126 52 67 253 67 253 253 14 126 46 81 253 220
## 3533 3549 3554 3557 3571 3577 3595 3596 3635 3648 3651 3723 3727
## 220 52 126 253 220 67 67 126 62 253 126 220 67
## 3728 3733 3750 3752 3753 3790 3833 3845 3857 3873 3893 3940 3965
## 126 31 220 126 62 220 253 25 81 81 220 67 44
## 3987 3989 4010 4011 4014 4068 4070 4080 4081 4102 4124 4159 4214
## 46 81 253 48 253 62 253 14 253 220 18 220 44
## 4229 4243 4245 4273 4276 4303 4304 4310 4323 4350 4352 4369 4374
## 253 253 44 253 81 220 126 220 253 2 5 253 31
## 4384 4432 4461 4470 4472 4476 4489 4526 4536 4542 4550 4590 4600
## 220 253 18 81 220 220 220 253 220 81 12 21 220
## 4615 4633 4667 4705 4708 4730 4750 4751 4756 4760 4792 4800 4801
## 44 253 253 48 220 253 220 31 31 253 126 11 52
## 4820 4822 4823 4833 4843 4846 4862 4864 4875 4879 4890 4904 4910
## 126 220 12 220 126 220 48 253 31 18 81 46 253
## 4921 4930 4954 4964 4982 5014 5015 5044 5049 5055 5057 5072 5103
## 126 30 46 31 46 28 220 48 253 52 253 253 18
## 5115 5120 5132 5136 5146 5179 5189 5223 5231 5251 5292 5293 5327
## 126 126 14 44 67 220 67 253 220 44 44 220 220
## 5331 5342 5359 5423 5424 5438 5446 5453 5461 5466 5507 5508 5531
## 81 253 46 253 46 253 67 126 11 220 52 126 253
## 5547 5552 5554 5556 5564 5579 5583 5597 5624 5666 5670 5688 5707
## 220 220 81 253 52 2 253 31 220 220 220 220 220
## 5714 5726 5731 5771 5793 5846 5849 5867 5881 5886 5899 5901 5902
## 253 253 81 253 220 253 253 220 220 30 67 220 220
## 5904 5905 5906 5911 5912 5918 5920 5939 5948 5962 5965 5973 5994
## 220 30 253 126 81 220 253 48 67 220 253 253 220
## 5996 5999 6003 6009 6018 6022 6054 6078 6085 6118 6121 6125 6134
## 253 48 220 126 220 46 126 46 220 253 253 126 52
## 6145 6163 6169 6173 6190 6202 6221 6223 6258 6322 6336 6345 6349
## 31 253 52 25 220 14 81 253 253 81 67 21 46
## 6350 6354 6367 6370 6373 6391 6404 6451 6472 6498 6505 6554 6563
## 52 81 220 220 30 220 81 253 220 220 253 253 81
## 6576 6591 6600 6630 6647 6679 6690 6695 6742 6757 6764 6784 6785
## 126 220 44 253 21 220 52 220 253 253 81 220 220
## 6815 6821 6857 6860 6869 6876 6880 6901 6914 6918 6920 6938 6939
## 253 253 46 253 253 21 220 81 5 3 253 220 14
## 6943 6946 6949 6955 6962 7026 7041 7048 7082 7103 7109 7116 7129
## 220 220 253 126 220 253 81 253 253 253 52 48 253
## 7137 7194 7202 7242 7261 7277 7295 7313 7316 7335 7337 7345 7352
## 67 220 253 253 220 21 30 81 220 220 21 48 220
## 7367 7373 7375 7392 7397 7398 7401 7430 7456 7458 7461 7472 7480
## 253 126 253 253 48 220 81 44 81 46 126 18 253
## 7482 7483 7508 7514 7564 7566 7569 7578 7619 7623 7624 7649 7697
## 220 220 220 253 28 126 30 220 253 253 220 67 81
## 7718 7731 7746 7756 7762 7765 7772 7779 7825 7830 7837 7850 7859
## 220 220 46 126 4 126 126 253 31 48 126 67 46
## 7862 7887 7890 7902 7904 7905 7941 7962 7964 7966 7983 8006 8015
## 31 220 67 62 253 220 220 44 81 126 220 81 220
## 8030 8077 8113 8123 8133 8134 8168 8174 8181 8183 8208 8220 8223
## 31 220 220 253 126 126 46 62 52 220 220 253 253
## 8253 8267 8293 8294 8309 8338 8342 8362 8365 8370 8373 8382 8393
## 46 52 220 253 81 126 220 48 52 46 126 220 30
## 8413 8426 8485 8542 8546 8566 8593 8609 8627 8663 8679 8708 8742
## 81 126 253 220 253 126 253 126 14 44 52 31 81
## 8746 8751 8761 8783 8787 8798 8825 8831 8850 8868 8878 8880 8894
## 62 220 220 30 30 220 126 220 81 253 67 126 126
## 8914 8931 8939 8953 8957 8992 8997 9015 9018 9025 9048 9061 9095
## 46 220 46 81 30 253 126 253 2 253 62 30 220
## 9098 9117 9129 9140 9163 9204 9210 9228 9229 9250 9253 9309 9310
## 253 220 220 126 48 253 253 220 220 126 126 44 253
## 9320 9341 9350 9355 9358 9373 9384 9407 9413 9424 9481 9486 9500
## 25 253 126 253 48 81 44 67 46 220 30 81 81
## 9553 9568 9570 9596 9607 9619 9641 9652 9659 9661 9664 9679 9682
## 62 220 67 52 5 220 67 62 253 253 48 46 253
## 9684 9714 9726 9763 9766 9782 9785 9792 9794 9809 9816 9834 9848
## 220 18 220 220 52 6 253 67 253 52 220 220 126
## 9851 9857 9877 9895 9905 9920 10003 10012 10054 10105 10110 10117 10118
## 48 2 253 6 220 52 220 220 126 220 31 81 6
## 10130 10155 10178 10201 10206 10209 10212 10241 10262 10270 10279 10290 10303
## 253 253 126 253 220 253 12 220 52 253 46 220 220
## 10364 10371 10386 10404 10423 10439 10449 10475 10480 10487 10500 10512 10516
## 220 253 220 126 67 220 126 2 67 220 253 30 44
## 10527 10544 10549 10568 10572 10582 10590 10638 10650 10694 10708 10724 10730
## 253 126 220 126 220 67 126 220 253 44 21 46 253
## 10736 10759 10771 10774 10776 10782 10802 10808 10836 10862 10874 10882 10886
## 220 253 30 253 48 81 253 220 11 253 62 126 18
## 10891 10906 10920 10929 10944 10952 10974 10995 11011 11025 11044 11073 11074
## 67 253 126 253 253 52 253 220 52 220 30 11 126
## 11123 11132 11142 11146 11156 11164 11171 11178 11217 11225 11234 11257 11296
## 48 81 48 253 28 3 28 67 220 253 253 253 220
## 11302 11305 11342 11352 11388 11390 11396 11417 11443 11456 11457 11461 11504
## 21 67 67 46 31 253 18 220 44 21 12 3 52
## 11527 11559 11560 11592 11606 11608 11642 11664 11671 11678 11700 11704 11707
## 81 126 220 62 253 126 253 81 220 67 253 253 253
## 11728 11729 11735 11753 11757 11818 11820 11839 11843 11859 11862 11887 11896
## 52 25 253 48 253 81 67 81 31 126 81 220 220
## 11935 11980 11988 12016 12030 12032 12049 12051 12075 12084 12091 12109 12114
## 253 21 253 126 253 220 126 31 220 220 253 220 81
## 12117 12128 12147 12160 12166 12190 12216 12236 12283 12307 12335 12367 12372
## 46 18 253 81 11 220 253 126 62 31 253 220 18
## 12376 12380 12382 12389 12395 12397 12408 12427 12439 12450 12452 12465 12468
## 220 126 46 62 62 126 253 220 21 81 81 253 67
## 12490 12496 12522 12527 12528 12537 12546 12550 12552 12554 12561 12568 12570
## 126 126 52 48 253 253 52 220 220 21 126 220 253
## 12579 12593 12607 12609 12622 12648 12705 12707 12739 12751 12761 12782 12799
## 253 44 253 220 46 220 81 253 25 81 11 81 253
## 12804 12806 12816 12843 12887 12888 12894 12901 12906 12918 12933 12943 12948
## 52 253 14 46 253 253 220 220 253 46 67 48 220
## 12956 12964 12968 12980 12984 13000 13035 13052 13087 13092 13104 13111 13133
## 126 81 253 220 253 253 253 81 126 220 220 126 220
## 13136 13137 13138 13143 13150 13154 13159 13188 13252 13261 13263 13284 13335
## 126 81 21 253 30 67 220 253 253 220 11 67 67
## 13336 13341 13360 13396 13450 13469 13497 13540 13553 13565 13586 13602 13621
## 253 220 126 253 253 14 67 220 220 21 52 126 67
## 13635 13636 13647 13656 13660 13674 13675 13682 13690 13732 13737 13742 13753
## 12 30 253 67 46 253 253 81 67 253 31 126 253
## 13755 13761 13777 13778 13782 13784 13791 13807 13892 13916 13962 13967 13989
## 220 220 220 21 126 220 81 253 253 5 253 220 220
## 14009 14035 14037 14060 14061 14064 14085 14095 14104 14110 14118 14119 14122
## 48 253 220 220 62 21 253 126 18 52 67 220 253
## 14125 14135 14137 14158 14187 14203 14204 14210 14216 14221 14256 14305 14311
## 253 253 220 52 220 126 67 253 31 52 11 253 21
## 14318 14321 14346 14358 14362 14368 14374 14403 14404 14414 14422 14435 14443
## 253 220 126 126 220 220 81 220 253 220 253 253 46
## 14481 14543 14571 14581 14592 14609 14628 14633 14635 14664 14683 14692 14725
## 81 253 6 62 253 2 253 46 126 253 18 126 220
## 14741 14743 14747 14751 14762 14780 14781 14787 14817 14819 14860 14895 14907
## 220 253 220 253 253 220 4 253 81 220 18 52 253
## 14925 14945 14947 14955 14963 15005 15024 15037 15096 15134 15143 15194 15196
## 220 253 44 67 67 126 220 220 253 52 126 126 46
## 15200 15217 15220 15242 15249
## 52 46 67 31 253En este gráfico podemos observar la red usuarios-usuarios. Cada nodo tiene un atributo asignado que es el K-core obtenido mediante la función de coreness. Además, cada nodo tiene asociado un color en función del valor del k-core. Los nodos con valores más altos, son los nodos que están más densamente conectados.
kcore <- coreness(users_projected) # Extraemos k-cores
V(users_projected)$core <- kcore # Agregamos los k-cores calculados como atributo de los nodos
colorfunc <- colorRampPalette(c( "Red 2",
"lightcoral",
"tomato",
"orange",
"Cadet Blue 1",
"Yellow 3",
"gold",
"lightgreen",
"Light Steel Blue 2",
"Royal Blue 1",
"Deep Sky Blue 1",
"Slate Blue"))
colors <- colorfunc(max(kcore))
V(users_projected)$color <- colors[kcore]
plot(users_projected,
vertex.size = 3,
layout = layout_components,
main="Proyeccion Red Usuarios - Nodos Según el Core al que Pertenecen")
plot(users_projected,
vertex.label=NA,
vertex.size = 3,
layout = layout_components,
main="Proyeccion Red Usuarios - Nodos Según el Core al que Pertenecen")
egog = make_ego_graph(users_projected, order=2)
k_g = egog[[1]]
degree_k_g = degree(k_g)
colorfunc <- colorRampPalette(c( "Red 2",
"lightcoral",
"tomato",
"orange",
"Cadet Blue 1",
"Yellow 3",
"gold",
"lightgreen",
"Light Steel Blue 2",
"Royal Blue 1",
"Deep Sky Blue 1",
"Slate Blue"))
scaled_degree = degree_k_g + 1
colors <- colorfunc(max(scaled_degree))
V(k_g)$color <- colors[scaled_degree]
plot(k_g,
vertex.size = 3,
vertex.label = degree_k_g,
edge.arrow.size=0.3,
layout = layout_components,
main = "Subgrafo Inducido: Nodos Según Degree")
plot(k_g,
vertex.size = 3,
vertex.label = NA,
edge.arrow.size=0.3,
layout = layout_components,
main = "Subgrafo Inducido: Nodos Según Degree")